Identification and Validation of Potential Ferroptosis-Related Genes in Glucocorticoid-Induced Osteonecrosis of the Femoral Head.
Medicina (Kaunas, Lithuania)
Glucocorticoid-induced osteonecrosis of the femoral head (GIONFH) is a serve complication of long-term administration of glucocorticoids. Previous experimental studies have shown that ferroptosis might be involved in the pathological process of GIONFH. The purpose of this study is to identify the ferroptosis-related genes and pathways of GIONFH by bioinformatics to further illustrate the mechanism of ferroptosis in SONFH through bioinformatics analysis. . The GSE123568 mRNA expression profile dataset, including 30 GIONFH samples and 10 non-GIONFH samples, was downloaded from the Gene Expression Omnibus (GEO) database. Ferroptosis-related genes were obtained from the FerrDb database. First, differentially expressed genes (DEGs) were identified between the serum samples from GIONFH cases and those from controls. Ferroptosis-related DEGs were obtained from the intersection of ferroptosis-related genes and DEGs. Only ferroptosis DEGs were used for all analyses. Then, we conducted a Kyoto encyclopedia of genome (KEGG) and gene ontology (GO) pathway enrichment analysis. We constructed a protein-protein interaction (PPI) network to screen out hub genes. Additionally, the expression levels of the hub genes were validated in an independent dataset GSE10311. . A total of 27 ferroptosis-related DEGs were obtained between the peripheral blood samples of GIONFH cases and non-GIONFH controls. Then, GO, and KEGG pathway enrichment analysis revealed that ferroptosis-related DEGs were mainly enriched in the regulation of the apoptotic process, oxidation-reduction process, and cell redox homeostasis, as well as HIF-1, TNF, FoxO signaling pathways, and osteoclast differentiation. Eight hub genes, including TLR4, PTGS2, SNCA, MAPK1, CYBB, SLC2A1, TXNIP, and MAP3K5, were identified by PPI network analysis. The expression levels of TLR4, TXNIP and MAP3K5 were further validated in the dataset GSE10311. . A total of 27 ferroptosis-related DEGs involved in GIONFH were identified via bioinformatics analysis. TLR4, TXNIP, and MAP3K5 might serve as potential biomarkers and drug targets for GIONFH.
10.3390/medicina59020297
Differential susceptibility of male and female germ cells to glucocorticoid-mediated signaling.
eLife
While physiologic stress has long been known to impair mammalian reproductive capacity through hormonal dysregulation, mounting evidence now suggests that stress experienced prior to or during gestation may also negatively impact the health of future offspring. Rodent models of gestational physiologic stress can induce neurologic and behavioral changes that persist for up to three generations, suggesting that stress signals can induce lasting epigenetic changes in the germline. Treatment with glucocorticoid stress hormones is sufficient to recapitulate the transgenerational changes seen in physiologic stress models. These hormones are known to bind and activate the glucocorticoid receptor (GR), a ligand-inducible transcription factor, thus implicating GR-mediated signaling as a potential contributor to the transgenerational inheritance of stress-induced phenotypes. Here, we demonstrate dynamic spatiotemporal regulation of GR expression in the mouse germline, showing expression in the fetal oocyte as well as the perinatal and adult spermatogonia. Functionally, we find that fetal oocytes are intrinsically buffered against changes in GR signaling, as neither genetic deletion of GR nor GR agonism with dexamethasone altered the transcriptional landscape or the progression of fetal oocytes through meiosis. In contrast, our studies revealed that the male germline is susceptible to glucocorticoid-mediated signaling, specifically by regulating RNA splicing within the spermatogonia, although this does not abrogate fertility. Together, our work suggests a sexually dimorphic function for GR in the germline, and represents an important step towards understanding the mechanisms by which stress can modulate the transmission of genetic information through the germline.
10.7554/eLife.90164
Identification of the shared gene signatures and molecular mechanisms between multiple sclerosis and non-small cell lung cancer.
Frontiers in immunology
Introduction:The association between multiple sclerosis (MS) and non-small cell lung cancer (NSCLC) has been the subject of investigation in clinical cohorts, yet the molecular mechanisms underpinning this relationship remain incompletely understood. To address this, our study aimed to identify shared genetic signatures, shared local immune microenvironment, and molecular mechanisms between MS and NSCLC. Methods:We selected multiple Gene Expression Omnibus (GEO) datasets, including GSE19188, GSE214334, GSE199460, and GSE148071, to obtain gene expression levels and clinical information from patients or mice with MS and NSCLC. We employed Weighted Gene Co-expression Network Analysis (WGCNA) to investigate co-expression networks linked to MS and NSCLC and used single-cell RNA sequencing (scRNA-seq) analysis to explore the local immune microenvironment of MS and NSCLC and identify possible shared components. Results:Our analysis identified the most significant shared gene in MS and NSCLC, phosphodiesterase 4A (PDE4A), and we analyzed its expression in NSCLC patients and its impact on patient prognosis, as well as its molecular mechanism. Our results demonstrated that high expression of PDE4A was associated with poor prognoses in NSCLC patients, and Gene Set Enrichment Analysis (GSEA) revealed that PDE4A is involved in immune-related pathways and has a significant regulatory effect on human immune responses. We further observed that PDE4A was closely linked to the sensitivity of several chemotherapy drugs. Conclusion:Given the limitation of studies investigating the molecular mechanisms underlying the correlation between MS and NSCLC, our findings suggest that there are shared pathogenic processes and molecular mechanisms between these two diseases and that PDE4A represents a potential therapeutic target and immune-related biomarker for patients with both MS and NSCLC.
10.3389/fimmu.2023.1180449
Exploring Shared Genetic Signatures of Alzheimer's Disease and Multiple Sclerosis: A Bioinformatic Analysis Study.
European neurology
INTRODUCTION:Many clinical studies reported the coexistence of Alzheimer's disease (AD) and multiple sclerosis (MS), but the common molecular signature between AD and MS remains elusive. The purpose of our study was to explore the genetic linkage between AD and MS through bioinformatic analysis, providing new insights into the shared signatures and possible pathogenesis of two diseases. METHODS:The common differentially expressed genes (DEGs) were determined between AD and MS from datasets obtained from Gene Expression Omnibus (GEO) database. Further, functional and pathway enrichment analysis, protein-protein interaction network construction, and identification of hub genes were carried out. The expression level of hub genes was validated in two other external AD and MS datasets. Transcription factor (TF)-gene interactions and gene-miRNA interactions were performed in NetworkAnalyst. Finally, receiver operating characteristic (ROC) curve analysis was applied to evaluate the predictive value of hub genes. RESULTS:A total of 75 common DEGs were identified between AD and MS. Functional and pathway enrichment analysis emphasized the importance of exocytosis and synaptic vesicle cycle, respectively. Six significant hub genes, including CCL2, CD44, GFAP, NEFM, STXBP1, and TCEAL6, were identified and verified as common hub genes shared by AD and MS. FOXC1 and hsa-mir-16-5p are the most common TF and miRNA in regulating hub genes, respectively. In the ROC curve analysis, all hub genes showed good efficiency in helping distinguish patients from controls. CONCLUSION:Our study first identified a common genetic signature between AD and MS, paving the road for investigating shared mechanism of AD and MS.
10.1159/000533397
Comprehensive analysis of anoikis-related genes in diagnosis osteoarthritis: based on machine learning and single-cell RNA sequencing data.
Artificial cells, nanomedicine, and biotechnology
Osteoarthritis (OA) is a degenerative disease closely associated with Anoikis. The objective of this work was to discover novel transcriptome-based anoikis-related biomarkers and pathways for OA progression.The microarray datasets GSE114007 and GSE89408 were downloaded using the Gene Expression Omnibus (GEO) database. A collection of genes linked to anoikis has been collected from the GeneCards database. The intersection genes of the differential anoikis-related genes (DEARGs) were identified using a Venn diagram. Infiltration analyses were used to identify and study the differentially expressed genes (DEGs). Anoikis clustering was used to identify the DEGs. By using gene clustering, two OA subgroups were formed using the DEGs. GSE152805 was used to analyse OA cartilage on a single cell level. 10 DEARGs were identified by lasso analysis, and two Anoikis subtypes were constructed. MEgreen module was found in disease WGCNA analysis, and MEturquoise module was most significant in gene clusters WGCNA. The XGB, SVM, RF, and GLM models identified five hub genes (CDH2, SHCBP1, SCG2, C10orf10, P FKFB3), and the diagnostic model built using these five genes performed well in the training and validation cohorts. analysing single-cell RNA sequencing data from GSE152805, including 25,852 cells of 6 OA cartilage.
10.1080/21691401.2024.2318210
A novel stratification framework based on anoikis-related genes for predicting the prognosis in patients with osteosarcoma.
Frontiers in immunology
Background:Anoikis resistance is a prerequisite for the successful development of osteosarcoma (OS) metastases, whether the expression of anoikis-related genes (ARGs) correlates with OS prognosis remains unclear. This study aimed to investigate the feasibility of using ARGs as prognostic tools for the risk stratification of OS. Methods:The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases provided transcriptome information relevant to OS. The GeneCards database was used to identify ARGs. Differentially expressed ARGs (DEARGs) were identified by overlapping ARGs with common differentially expressed genes (DEGs) between OS and normal samples from the GSE16088, GSE19276, and GSE99671 datasets. Anoikis-related clusters of patients were obtained by consistent clustering, and gene set variation analysis (GSVA) of the different clusters was completed. Next, a risk model was created using Cox regression analyses. Risk scores and clinical features were assessed for independent prognostic values, and a nomogram model was constructed. Subsequently, a functional enrichment analysis of the high- and low-risk groups was performed. In addition, the immunological characteristics of OS samples were compared between the high- and low-risk groups, and their sensitivity to therapeutic agents was explored. Results:Seven DEARGs between OS and normal samples were obtained by intersecting 501 ARGs with 68 common DEGs. and were significantly differentially expressed between both clusters (<0.05) and were identified as prognosis-related genes. The risk model showed that the risk score and tumor metastasis were independent prognostic factors of patients with OS. A nomogram combining risk score and tumor metastasis effectively predicted the prognosis. In addition, patients in the high-risk group had low immune scores and high tumor purity. The levels of immune cell infiltration, expression of human leukocyte antigen (HLA) genes, immune response gene sets, and immune checkpoints were lower in the high-risk group than those in the low-risk group. The low-risk group was sensitive to the immune checkpoint PD-1 inhibitor, and the high-risk group exhibited lower inhibitory concentration values by 50% for 24 drugs, including AG.014699, AMG.706, and AZD6482. Conclusion:The prognostic stratification framework of patients with OS based on ARGs, such as and , may lead to more efficient clinical management.
10.3389/fimmu.2023.1199869
Identification of Anoikis-Related Subgroups and Prognosis Model in Liver Hepatocellular Carcinoma.
International journal of molecular sciences
Resistance to anoikis is a key characteristic of many cancer cells, promoting cell survival. However, the mechanism of anoikis in hepatocellular carcinoma (HCC) remains unknown. In this study, we applied differentially expressed overlapping anoikis-related genes to classify The Cancer Genome Atlas (TCGA) samples using an unsupervised cluster algorithm. Then, we employed weighted gene coexpression network analysis (WGCNA) to identify highly correlated genes and constructed a prognostic risk model based on univariate Cox proportional hazards regression. This model was validated using external datasets from the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO). Finally, we used a CIBERSORT algorithm to investigate the correlation between risk score and immune infiltration. Our results showed that the TCGA cohorts could be divided into two subgroups, with subgroup A having a lower survival probability. Five genes (, , , and DAP3) were identified as anoikis-related prognostic genes. Moreover, the prognostic risk model effectively predicted overall survival, which was validated using ICGC and GEO datasets. In addition, there was a strong correlation between infiltrating immune cells and prognostic genes and risk score. In conclusion, we identified anoikis-related subgroups and prognostic genes in HCC, which could be significant for understanding the molecular mechanisms and treatment of HCC.
10.3390/ijms24032862
Tfh cell subset biomarkers and inflammatory markers are associated with frailty status and frailty subtypes in the community-dwelling older population: a cross-sectional study.
Yin Ming-Juan,Xiong Yong-Zhen,Xu Xiu-Juan,Huang Ling-Feng,Zhang Yan,Wang Xiao-Jun,Xiu Liang-Chang,Huang Jing-Xiao,Lian Ting-Yu,Liang Dong-Mei,Zen Jin-Mei,Ni Jin-Dong
Aging
We conducted a cross-sectional study investigating community-dwelling older population to determine association between immunoscenescence marker, inflammatory cytokines and frailty. Frailty status was classified with 33-item modified frailty index and latent class analysis was applied to explore the latent classes (subtypes) of frailty. In multivariable analysis, higher Tfh2 cells were associated with a higher risk of frailty [1.13(1.03-1.25)] in females, but a lower risk of cognitive and functional frail [0.92(0.86-0.99)] and physiological frail [0.92(0.87-0.98)]. Additionally, a greater risk of multi-frail and physiological frail correlated with low Tfh1 [0.77(0.60-0.99); 0.87(0.79-0.96)] and Tfh17 cells [0.79(0.65-0.96); 0.86(0.78-0.94)], respectively. Higher B cells were associated with decreased frailty/pre-frailty both in females [0.89(0.81-0.98)] and males [0.82(0.71-0.96)], but did not correlate with frailty subtypes. Regarding inflammatory markers, participants in the TGF-β 2 quartile showed a decreased risk of pre-frailty/frailty in females [0.39(0.17-0.89)] and psychological frail [0.37(0.16-0.88)], compared with those in the top tertile. Moreover, we found participants in the 2 tertile for IL-12 levels showed a decreased risk of physiological frail [0.40 (0.17-0.97)]. Our study highlights the importance of Tfh cell subsets and inflammatory markers in frailty in a sex-specific manner, particularly in terms of frailty subtype.
10.18632/aging.102789
Comparison of cytokine levels in prostatic secretion between the IIIa and IIIb subtypes of prostatitis.
Asian journal of andrology
Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS), also known as National Institutes of Health (NIH) type III prostatitis, is a common disorder with an unclear etiology and no known curative treatments. Based on the presence or absence of leukocytes in expressed prostatic secretion (EPS), CP/CPPS is classified further into IIIa (inflammatory) and IIIb (noninflammatory) subtypes. However, the severity of symptoms is not entirely consistent with the white blood cell (WBC) count. Following the preliminary finding of a link between inflammatory cytokines and CP/CPPS, we performed this clinical study with the aim of identifying cytokines that are differentially expressed according to whether the prostatitis subtype is IIIa or IIIb. We found that granulocyte colony-stimulating factor (G-CSF), interleukin-18 (IL-18), and monocyte chemoattractant protein-1 (MCP-1) levels were significantly elevated and interferon-inducible protein-10 (IP-10) and platelet-derived growth factor-BB (PDGF-BB) levels were downregulated in the EPS of patients with type IIIa prostatitis. In a word, it is a meaningful study in which we investigate the levels of various cytokines in EPS according to whether prostatitis is the IIIa or IIIb subtype. The combination of G-CSF, IL-18, MCP-1, IP-10, and PDGF-BB expression levels could form a basis for classification, diagnosis, and therapeutic targets in clinical CP/CPPS.
10.4103/aja202336
Identification of diagnostic signature, molecular subtypes, and potential drugs in allergic rhinitis based on an inflammatory response gene set.
Frontiers in immunology
Background:Rhinitis is a complex condition characterized by various subtypes, including allergic rhinitis (AR), which involves inflammatory reactions. The objective of this research was to identify crucial genes associated with inflammatory response that are relevant for the treatment and diagnosis of AR. Methods:We acquired the AR-related expression datasets (GSE75011 and GSE50223) from the Gene Expression Omnibus (GEO) database. In GSE75011, we compared the gene expression profiles between the HC and AR groups and identified differentially expressed genes (DEGs). By intersecting these DEGs with inflammatory response-related genes (IRGGs), resulting in the identification of differentially expressed inflammatory response-related genes (DIRRGs). Afterwards, we utilized the protein-protein interaction (PPI) network, machine learning algorithms, namely least absolute shrinkage and selection operator (LASSO) regression and random forest, to identify the signature markers. We employed a nomogram to evaluate the diagnostic effectiveness of the method, which has been confirmed through validation using GSE50223. qRT-PCR was used to confirm the expression of diagnostic genes in clinical samples. In addition, a consensus clustering method was employed to categorize patients with AR. Subsequently, extensive investigation was conducted to explore the discrepancies in gene expression, enriched functions and pathways, as well as potential therapeutic drugs among these distinct subtypes. Results:A total of 22 DIRRGs were acquired, which participated in pathways including chemokine and TNF signaling pathway. Additionally, machine learning algorithms identified NFKBIA, HIF1A, MYC, and CCRL2 as signature genes associated with AR's inflammatory response, indicating their potential as AR biomarkers. The nomogram based on feature genes could offer clinical benefits to AR patients. We discovered two molecular subtypes, C1 and C2, and observed that the C2 subtype exhibited activation of immune- and inflammation-related pathways. Conclusions:NFKBIA, HIF1A, MYC, and CCRL2 are the key genes involved in the inflammatory response and have the strongest association with the advancement of disease in AR. The proposed molecular subgroups could provide fresh insights for personalized treatment of AR.
10.3389/fimmu.2024.1348391
A protein-based machine learning approach to the identification of inflammatory subtypes in pancreatic ductal adenocarcinoma.
Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]
BACKGROUND/OBJECTIVES:The inherently immunosuppressive tumor microenvironment along with the heterogeneity of pancreatic ductal adenocarcinoma (PDAC) limits the effectiveness of available treatment options and contributes to the disease lethality. Using a machine learning algorithm, we hypothesized that PDAC may be categorized based on its microenvironment inflammatory milieu. METHODS:Fifty-nine tumor samples from patients naïve to treatment were homogenized and probed for 41 unique inflammatory proteins using a multiplex assay. Subtype clustering was determined using t-distributed stochastic neighbor embedding (t-SNE) machine learning analysis of cytokine/chemokine levels. Statistics were performed using Wilcoxon rank sum test and Kaplan-Meier survival analysis. RESULTS:t-SNE analysis of tumor cytokines/chemokines revealed two distinct clusters, immunomodulating and immunostimulating. In pancreatic head tumors, patients in the immunostimulating group (N = 26) were more likely to be diabetic (p = 0.027), but experienced less intraoperative blood loss (p = 0.0008). Though there were no significant differences in survival (p = 0.161), the immunostimulating group trended toward longer median survival by 9.205 months (11.28 vs. 20.48 months). CONCLUSION:A machine learning algorithm identified two distinct subtypes within the PDAC inflammatory milieu, which may influence diabetes status as well as intraoperative blood loss. Opportunity exists to further explore how these inflammatory subtypes may influence treatment response, potentially elucidating targetable mechanisms of PDAC's immunosuppressive tumor microenvironment.
10.1016/j.pan.2023.06.007
Integrated analysis of plasma and urine reveals unique metabolomic profiles in idiopathic inflammatory myopathies subtypes.
Journal of cachexia, sarcopenia and muscle
OBJECTIVES:Idiopathic inflammatory myopathies (IIM) are a class of autoimmune diseases with high heterogeneity that can be divided into different subtypes based on clinical manifestations and myositis-specific autoantibodies (MSAs). However, even in each IIM subtype, the clinical symptoms and prognoses of patients are very different. Thus, the identification of more potential biomarkers associated with IIM classification, clinical symptoms, and prognosis is urgently needed. METHODS:Plasma and urine samples from 79 newly diagnosed IIM patients (mean disease duration 4 months) and 52 normal control (NC) samples were analysed by high-performance liquid chromatography of quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF-MS)/MS-based untargeted metabolomics. Orthogonal partial least-squares discriminate analysis (OPLS-DA) were performed to measure the significance of metabolites. Pathway enrichment analysis was conducted based on the KEGG human metabolic pathways. Ten machine learning (ML) algorithms [linear support vector machine (SVM), radial basis function SVM, random forest, nearest neighbour, Gaussian processes, decision trees, neural networks, adaptive boosting (AdaBoost), Gaussian naive Bayes and quadratic discriminant analysis] were used to classify each IIM subtype and select the most important metabolites as potential biomarkers. RESULTS:OPLS-DA showed a clear separation between NC and IIM subtypes in plasma and urine metabolic profiles. KEGG pathway enrichment analysis revealed multiple unique and shared disturbed metabolic pathways in IIM main [dermatomyositis (DM), anti-synthetase syndrome (ASS), and immune-mediated necrotizing myopathy (IMNM)] and MSA-defined subtypes (anti-Mi2+, anti-MDA5+, anti-TIF1γ+, anti-Jo1+, anti-PL7+, anti-PL12+, anti-EJ+, and anti-SRP+), such that fatty acid biosynthesis was significantly altered in both plasma and urine in all main IIM subtypes (enrichment ratio > 1). Random forest and AdaBoost performed best in classifying each IIM subtype among the 10 ML models. Using the feature selection methods in ML models, we identified 9 plasma and 10 urine metabolites that contributed most to separate IIM main subtypes and MSA-defined subtypes, such as plasma creatine (fold change = 3.344, P = 0.024) in IMNM subtype and urine tiglylcarnitine (fold change = 0.351, P = 0.037) in anti-EJ+ ASS subtype. Sixteen common metabolites were found in both the plasma and urine samples of IIM subtypes. Among them, some were correlated with clinical features, such as plasma hypogeic acid (r = -0.416, P = 0.005) and urine malonyl carnitine (r = -0.374, P = 0.042), which were negatively correlated with the prevalence of interstitial lung disease. CONCLUSIONS:In both plasma and urine samples, IIM main and MSA-defined subtypes have specific metabolic signatures and pathways. This study provides useful clues for understanding the molecular mechanisms, searching potential diagnosis biomarkers and therapeutic targets for IIM.
10.1002/jcsm.13045
Immune-Associated Gene Signatures and Subtypes to Predict the Progression of Atherosclerotic Plaques Based on Machine Learning.
Frontiers in pharmacology
Experimental and clinical evidence suggests that atherosclerosis is a chronic inflammatory disease. Our study was conducted for uncovering the roles of immune-associated genes during atherosclerotic plaque progression. Gene expression profiling of GSE28829, GSE43292, GSE41571, and GSE120521 datasets was retrieved from the GEO database. Three machine learning algorithms, least absolute shrinkage, and selection operator (LASSO), random forest, and support vector machine-recursive feature elimination (SVM-RFE) were utilized for screening characteristic genes among atherosclerotic plaque progression- and immune-associated genes. ROC curves were generated for estimating the diagnostic efficacy. Immune cell infiltrations were estimated via ssGSEA, and immune checkpoints were quantified. CMap analysis was implemented to screen potential small-molecule compounds. Atherosclerotic plaque specimens were classified using a consensus clustering approach. Seven characteristic genes (TNFSF13B, CCL5, CCL19, ITGAL, CD14, GZMB, and BTK) were identified, which enabled the prediction of progression of atherosclerotic plaques. Higher immune cell infiltrations and immune checkpoint expressions were found in advanced-stage than in early-stage atherosclerotic plaques and were positively linked to characteristic genes. Patients could clinically benefit from the characteristic gene-based nomogram. Several small molecular compounds were predicted based on the characteristic genes. Two subtypes, namely, C1 immune subtype and C2 non-immune subtype, were classified across atherosclerotic plaques. The characteristic genes presented higher expression in C1 than in C2 subtypes. Our findings provide several promising atherosclerotic plaque progression- and immune-associated genes as well as immune subtypes, which might enable to assist the design of more accurately tailored cardiovascular immunotherapy.
10.3389/fphar.2022.865624
Inflammatory Subtypes in Antipsychotic-Naïve First-Episode Schizophrenia are Associated with Altered Brain Morphology and Topological Organization.
Brain, behavior, and immunity
BACKGROUND:Peripheral inflammation is implicated in schizophrenia, however, not all individuals demonstrate inflammatory alterations. Recent studies identified inflammatory subtypes in chronic psychosis with high inflammation having worse cognitive performance and displaying neuroanatomical enlargement compared to low inflammation subtypes. It is unclear if inflammatory subtypes exist earlier in the disease course, thus, we aim to identify inflammatory subtypes in antipsychotic naïve First-Episode Schizophrenia (FES). METHODS:12 peripheral inflammatory markers, clinical, cognitive, and neuroanatomical measures were collected from a naturalistic study of antipsychotic-naïve FES patients. A combination of unsupervised principal component analysis and hierarchical clustering was used to categorize inflammatory subtypes from their cytokine data (17 FES High, 30 FES Low, and 33 healthy controls (HCs)). Linear regression analysis was used to assess subtype differences. Neuroanatomical correlations with clinical and cognitive measures were performed using partial Spearman correlations. Graph theoretical analyses were performed to assess global and local network properties across inflammatory subtypes. RESULTS:The FES High group made up 36% of the FES group and demonstrated significantly greater levels of IL1β, IL6, IL8, and TNFα compared to FES Low, and higher levels of IL1β and IL8 compared to HCs. FES High had greater right parahippocampal, caudal anterior cingulate, and bank superior sulcus thicknesses compared to FES Low. Compared to HCs, FES Low showed smaller bilateral amygdala volumes and widespread cortical thickness. FES High and FES Low groups demonstrated less efficient topological organization compared to HCs. Individual cytokines and/or inflammatory signatures were positively associated with cognition and symptom measures. CONCLUSIONS:Inflammatory subtypes are present in antipsychotic-naïve FES and are associated with inflammation-mediated cortical expansion. These findings support our previous findings in chronic psychosis and point towards a connection between inflammation and blood-brain barrier disruption. Thus, identifying inflammatory subtypes may provide a novel therapeutic avenue for biomarker-guided treatment involving anti-inflammatory medications.
10.1016/j.bbi.2021.11.019
Temporal relations between atrial fibrillation and ischaemic stroke and their prognostic impact on mortality.
Camen Stephan,Ojeda Francisco M,Niiranen Teemu,Gianfagna Francesco,Vishram-Nielsen Julie K,Costanzo Simona,Söderberg Stefan,Vartiainen Erkki,Donati Maria Benedetta,Løchen Maja-Lisa,Pasterkamp Gerard,Magnussen Christina,Kee Frank,Jousilahti Pekka,Hughes Maria,Kontto Jukka,Mathiesen Ellisiv B,Koenig Wolfgang,Palosaari Tarja,Blankenberg Stefan,de Gaetano Giovanni,Jørgensen Torben,Zeller Tanja,Kuulasmaa Kari,Linneberg Allan,Salomaa Veikko,Iacoviello Licia,Schnabel Renate B
Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
AIMS:Limited evidence is available on the temporal relationship between atrial fibrillation (AF) and ischaemic stroke and their impact on mortality in the community. We sought to understand the temporal relationship of AF and ischaemic stroke and to determine the sequence of disease onset in relation to mortality. METHODS AND RESULTS:Across five prospective community cohorts of the Biomarkers for Cardiovascular Risk Assessment in Europe (BiomarCaRE) project we assessed baseline cardiovascular risk factors in 100 132 individuals, median age 46.1 (25th-75th percentile 35.8-57.5) years, 48.4% men. We followed them for incident ischaemic stroke and AF and determined the relation of subsequent disease diagnosis with overall mortality. Over a median follow-up of 16.1 years, N = 4555 individuals were diagnosed solely with AF, N = 2269 had an ischaemic stroke but no AF diagnosed, and N = 898 developed both, ischaemic stroke and AF. Temporal relationships showed a clustering of diagnosis of both diseases within the years around the diagnosis of the other disease. In multivariable-adjusted Cox regression analyses with time-dependent covariates subsequent diagnosis of AF after ischaemic stroke was associated with increased mortality [hazard ratio (HR) 4.05, 95% confidence interval (CI) 2.17-7.54; P < 0.001] which was also apparent when ischaemic stroke followed after the diagnosis of AF (HR 3.08, 95% CI 1.90-5.00; P < 0.001). CONCLUSION:The temporal relations of ischaemic stroke and AF appear to be bidirectional. Ischaemic stroke may precede detection of AF by years. The subsequent diagnosis of both diseases significantly increases mortality risk. Future research needs to investigate the common underlying systemic disease processes.
10.1093/europace/euz312
Identification of key biomarkers in ischemic stroke: single-cell sequencing and weighted co-expression network analysis.
Aging
PURPOSE:At present, there is a lack of accurate early diagnostic markers for ischemic stroke. METHODS:By using dimensionality reduction cluster analysis, differential expression analysis, weighted co-expression network analysis, protein-protein interaction network analysis, cell heterogeneity and key pathogenic genes were identified in ischemic stroke. Immunomicroenvironment analysis was used to explore the immune landscape and immune associations of key genes in ischemic stroke. The analysis platform we use is R software (version 4.0.5). PCR experiments were used to verify the expression of key genes. RESULTS:Single cell sequencing data in ischemic stroke can be annotated as fibroblast cells, pre-B cell CD34, neutrophils cells, bone marrow (BM), keratinocytes, macrophage, neurons and mesenchymal stem cells (MSC). By the intersection of differential expression analysis and WGCNA analysis, 385 genes were obtained. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that these genes were highly correlated with multiple functions and pathways. Protein-protein interaction network analysis revealed that MRPS11 and MRPS12 were key genes, both of which were down-regulated in ischemic stroke. The Pseudo-time series analysis found that the expression of MRPS12 decreased gradually with the differentiation of pre-B cell CD34 cells in ischemic stroke, suggesting that the downregulation of MRPS12 expression may play an important role in ischemic stroke. At last, PCR showed that MRPS11 and MRPS12 were significantly down-regulated in peripheral blood of patients with ischemic stroke. CONCLUSIONS:Our study provides a reference for the study of pathogenesis and key targets of ischemic stroke.
10.18632/aging.204855
Cluster analysis of weather and pollution features and its role in predicting acute cardiac or cerebrovascular events.
Minerva medica
BACKGROUND:Despite mounting evidence, the impact of the interplay between weather and pollution features on the risk of acute cardiac and cerebrovascular events has not been entirely appraised. The aim of this study was to perform a comprehensive cluster analysis of weather and pollution features in a large metropolitan area, and their association with acute cardiac and cerebrovascular events. METHODS:Anonymized data on acute myocardial infarction (AMI) and acute cerebrovascular events were obtained from 3 tertiary care centers from a single large metropolitan area. Weather and pollution data were obtained averaging measurements from several city measurement stations managed by the competent regional agency for enviromental protection, and from the Metereological Center of Italian Military Aviation. Unsupervised machine learning was performed with hierarchical clustering to identify specific days with distinct weather and pollution features. Clusters were then compared for rates of acute cardiac and cerebrovascular events with Poisson models. RESULTS:As expected, significant pairwise correlations were found between weather and pollution features. Building upon these correlations, hierarchical clustering, from a total of 1169 days, generated 4 separate clusters: mostly winter days with low temperatures and high ozone concentrations (cluster 1, N.=60, 5.1%), days with moderately high temperatures and low pollutants concentrations (cluster 2, N.=419, 35.8%), mostly summer and spring days with high temperatures and high ozone concentrations (cluster 3, N.=673, 57.6%), and mostly winter days with low temperatures and low ozone concentrations (cluster 4, N.=17, 1.5%). Overall cluster-wise comparisons showed significant differences in adverse cardiac and cerebrovascular events (P<0.001), as well as in cerebrovascular events (P<0.001) and strokes (P=0.001). Between-cluster comparisons showed that cluster 1 was associated with an increased risk of any event, cerebrovascular events, and strokes in comparison to cluster 2, cluster 3 and cluster 4 (all P<0.05), as well as AMI in comparison to cluster 3 (P=0.047). In addition, cluster 2 was associated with a higher risk of strokes in comparison to cluster 4 (P=0.030). Analysis adjusting for season confirmed the increased risk of any event, cerebrovascular events and strokes for cluster 1 and cluster 2. CONCLUSIONS:Unsupervised machine learning can be leveraged to identify specific days with a unique clustering of adverse weather and pollution features which are associated with an increased risk of acute cardiovascular events, especially cerebrovascular events. These findings may improve collective and individual risk prediction and prevention.
10.23736/S0026-4806.22.08036-3
Phenotyping of atrial fibrillation with cluster analysis and external validation.
Heart (British Cardiac Society)
OBJECTIVES:Atrial fibrillation (AF) is a heterogeneous condition. We performed a cluster analysis in a cohort of patients with AF and assessed the prognostic implication of the identified cluster phenotypes. METHODS:We used two multicentre, prospective, observational registries of AF: the SAKURA AF registry (Real World Survey of Atrial Fibrillation Patients Treated with Warfarin and Non-vitamin K Antagonist Oral Anticoagulants) (n=3055, derivation cohort) and the RAFFINE registry (Registry of Japanese Patients with Atrial Fibrillation Focused on anticoagulant therapy in New Era) (n=3852, validation cohort). Cluster analysis was performed by the K-prototype method with 14 clinical variables. The endpoints were all-cause mortality and composite cardiovascular events. RESULTS:The analysis subclassified derivation cohort patients into five clusters. Cluster 1 (n=414, 13.6%) was characterised by younger men with a low prevalence of comorbidities; cluster 2 (n=1003, 32.8%) by a high prevalence of hypertension; cluster 3 (n=517, 16.9%) by older patients without hypertension; cluster 4 (n=652, 21.3%) by the oldest patients, who were mainly female and with a high prevalence of heart failure history; and cluster 5 (n=469, 15.3%) by older patients with high prevalence of diabetes and ischaemic heart disease. During follow-up, the risk of all-cause mortality and composite cardiovascular events increased across clusters (log-rank p<0.001, p<0.001). Similar results were found in the external validation cohort. CONCLUSIONS:Machine learning-based cluster analysis identified five different phenotypes of AF with unique clinical characteristics and different clinical outcomes. The use of these phenotypes may help identify high-risk patients with AF.
10.1136/heartjnl-2023-322447
Biomarker and genomic analyses reveal molecular signatures of non-cardioembolic ischemic stroke.
Signal transduction and targeted therapy
Acute ischemic stroke (AIS) is a major cause of disability and mortality worldwide. Non-cardioembolic ischemic stroke (NCIS), which constitutes the majority of AIS cases, is highly heterogeneous, thus requiring precision medicine treatments. This study aimed to investigate the molecular mechanisms underlying NCIS heterogeneity. We integrated data from the Third China National Stroke Registry, including clinical phenotypes, biomarkers, and whole-genome sequencing data for 7695 patients with NCIS. We identified 30 molecular clusters based on 63 biomarkers and explored the comprehensive landscape of biological heterogeneity and subpopulations in NCIS. Dimensionality reduction revealed fine-scale subpopulation structures associated with specific biomarkers. The subpopulations with biomarkers for inflammation, abnormal liver and kidney function, homocysteine metabolism, lipid metabolism, and gut microbiota metabolism were associated with a high risk of unfavorable clinical outcomes, including stroke recurrence, disability, and mortality. Several genes encoding potential drug targets were identified as putative causal genes that drive the clusters, such as CDK10, ERCC3, and CHEK2. We comprehensively characterized the genetic architecture of these subpopulations, identified their molecular signatures, and revealed the potential of the polybiomarkers and polygenic prediction for assessing clinical outcomes. Our study demonstrates the power of large-scale molecular biomarkers and genomics to understand the underlying biological mechanisms of and advance precision medicine for NCIS.
10.1038/s41392-023-01465-w
Symptom Subtypes of Obstructive Sleep Apnea Predict Incidence of Cardiovascular Outcomes.
American journal of respiratory and critical care medicine
Symptom subtypes have been described in clinical and population samples of patients with obstructive sleep apnea (OSA). It is unclear whether these subtypes have different cardiovascular consequences. To characterize OSA symptom subtypes and assess their association with prevalent and incident cardiovascular disease in the Sleep Heart Health Study. Data from 1,207 patients with OSA (apnea-hypopnea index ≥ 15 events/h) were used to evaluate the existence of symptom subtypes using latent class analysis. Associations between subtypes and prevalence of overall cardiovascular disease and its components (coronary heart disease, heart failure, and stroke) were assessed using logistic regression. Kaplan-Meier survival analysis and Cox proportional hazards models were used to evaluate whether subtypes were associated with incident events, including cardiovascular mortality. Four symptom subtypes were identified (disturbed sleep [12.2%], minimally symptomatic [32.6%], excessively sleepy [16.7%], and moderately sleepy [38.5%]), similar to prior studies. In adjusted models, although no significant associations with prevalent cardiovascular disease were found, the excessively sleepy subtype was associated with more than threefold increased risk of prevalent heart failure compared with each of the other subtypes. Symptom subtype was also associated with incident cardiovascular disease ( < 0.001), coronary heart disease ( = 0.015), and heart failure ( = 0.018), with the excessively sleepy again demonstrating increased risk (hazard ratios, 1.7-2.4) compared with other subtypes. When compared with individuals without OSA (apnea-hypopnea index < 5), significantly increased risk for prevalent and incident cardiovascular events was observed mostly for patients in the excessively sleepy subtype. OSA symptom subtypes are reproducible and associated with cardiovascular risk, providing important evidence of their clinical relevance.
10.1164/rccm.201808-1509OC
Changes in the triglyceride glucose-body mass index estimate the risk of stroke in middle-aged and older Chinese adults: a nationwide prospective cohort study.
Cardiovascular diabetology
BACKGROUND:Stroke was reported to be highly correlated with the triglyceride glucose-body mass index (TyG-BMI). Nevertheless, literature exploring the association between changes in the TyG-BMI and stroke incidence is scant, with most studies focusing on individual values of the TyG-BMI. We aimed to investigate whether changes in the TyG-BMI were associated with stroke incidence. METHODS:Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS), which is an ongoing nationally representative prospective cohort study. The exposures were changes in the TyG-BMI and cumulative TyG-BMI from 2012 to 2015. Changes in the TyG-BMI were classified using K-means clustering analysis, and the cumulative TyG-BMI was calculated as follows: (TyG-BMI + TyG-BMI)/2 × time (2015-2012). Logistic regressions were used to determine the association between different TyG-BMI change classes and stroke incidence. Meanwhile, restricted cubic spline regression was applied to examine the potential nonlinear association of the cumulative TyG-BMI and stroke incidence. Weighted quantile sum regression was used to provide a comprehensive explanation of the TyG-BMI by calculating the weights of FBG, triglyceride-glucose (TG), and BMI. RESULTS:Of the 4583 participants (mean [SD] age at baseline, 58.68 [9.51] years), 2026 (44.9%) were men. During the 3 years of follow-up, 277 (6.0%) incident stroke cases were identified. After adjusting for potential confounders, compared to the participants with a consistently low TyG-BMI, the OR for a moderate TyG-BMI with a slow rising trend was 1.01 (95% CI 0.65-1.57), the OR for a high TyG-BMI with a slow rising trend was 1.62 (95% CI 1.11-2.32), and the OR for the highest TyG-BMI with a slow declining trend was 1.71 (95% CI 1.01-2.89). The association between the cumulative TyG-BMI and stroke risk was nonlinear (P = 0.017; P = 0.012). TG emerged as the primary contributor when the weights were assigned to the constituent elements of the TyG-BMI (weight = 0.466; weight = 0.530). CONCLUSIONS:Substantial changes in the TyG-BMI are independently associated with the risk of stroke in middle-aged and older adults. Monitoring long-term changes in the TyG-BMI may assist with the early identification of individuals at high risk of stroke.
10.1186/s12933-023-01983-5
The change of triglyceride-glucose index may predict incidence of stroke in the general population over 45 years old.
Cardiovascular diabetology
BACKGROUND:Stroke has been found to be highly correlated with the triglyceride-glucose (TyG) index. The relation between the TyG index changes and stroke, however, has seldom been reported, and current researches mentioning the TyG index concentrate on individual values. We aimed to investigate whether the level and the change of TyG index was associated with the incidence of stroke. METHODS:Sociodemographic, medical background, anthropometric and laboratory information were retrospectively collected. Classification was conducted using k-means clustering analysis. Logistic regressions were to determine the relationship between different classes with changes in the TyG index and incidence of stroke, taking the class with the smallest change as a reference. Meanwhile, restricted cubic spline regression was applied to examine the links of cumulative TyG index and stroke. RESULTS:369 (7.8%) of 4710 participants had a stroke during 3 years. Compared to class 1 with the best control of the TyG Index, the OR for class 2 with good control was 1.427 (95% CI, 1.051-1.938), the OR for class 3 with moderate control was 1.714 (95% CI, 1.245-2.359), the OR for class 4 with worse control was 1.814 (95% CI, 1.257-2.617), and the OR for class 5 with consistently high levels was 2.161 (95% CI, 1.446-3.228). However, after adjusting for multiple factors, only class 3 still had an association with stroke (OR 1.430, 95%CI, 1.022-2.000). The relation between the cumulative TyG index and stroke was linear in restricted cubic spline regression. In subgroup analysis, similar results were shown in participants without diabetes or dyslipidemia. There is neither additive nor multiplicative interaction between TyG index class and covariates. CONCLUSION:A constant higher level with worst control in TyG index indicated a higher risk of stroke.
10.1186/s12933-023-01870-z
Cluster features in fibrosing interstitial lung disease and associations with prognosis.
BMC pulmonary medicine
BACKGROUND:Clustering is helpful in identifying subtypes in complex fibrosing interstitial lung disease (F-ILD) and associating them with prognosis at an early stage of the disease to improve treatment management. We aimed to identify associations between clinical characteristics and outcomes in patients with F-ILD. METHODS:Retrospectively, 575 out of 926 patients with F-ILD were eligible for analysis. Four clusters were identified based on baseline data using cluster analysis. The clinical characteristics and outcomes were compared among the groups. RESULTS:Cluster 1 was characterized by a high prevalence of comorbidities and hypoxemia at rest, with the worst lung function at baseline; Cluster 2 by young female patients with less or no smoking history; Cluster 3 by male patients with highest smoking history, the most noticeable signs of velcro crackles and clubbing of fingers, and the severe lung involvement on chest image; Cluster 4 by male patients with a high percentage of occupational or environmental exposure. Clusters 1 (median overall survival [OS] = 7.0 years) and 3 (OS = 5.9 years) had shorter OS than Clusters 2 (OS = not reached, Cluster 1: p < 0.001, Cluster 3: p < 0.001) and 4 (OS = not reached, Cluster 1: p = 0.004, Cluster 3: p < 0.001). Clusters 1 and 3 had a higher cumulative incidence of acute exacerbation than Clusters 2 (Cluster 1: p < 0.001, Cluster 3: p = 0.014) and 4 (Cluster 1: p < 0.001, Cluster 3: p = 0.006). Stratification by using clusters also independently predicted acute exacerbation (p < 0.001) and overall survival (p < 0.001). CONCLUSIONS:The high degree of disease heterogeneity of F-ILD can be underscored by four clusters based on clinical characteristics, which may be helpful in predicting the risk of fibrosis progression, acute exacerbation and overall survival.
10.1186/s12890-023-02735-7
Exploring the potential mechanisms of Tongmai Jiangtang capsules in treating diabetic nephropathy through multi-dimensional data.
Frontiers in endocrinology
Background:Diabetic nephropathy (DN) is a prevalent and debilitating disease that represents the leading cause of chronic kidney disease which imposes public health challenges Tongmai Jiangtang capsule (TMJT) is commonly used for the treatment of DN, albeit its underlying mechanisms of action are still elusive. Methods:This study retrieved databases to identify the components and collect the targets of TMJT and DN. Target networks were constructed to screen the core components and targets. Samples from the GEO database were utilized to perform analyses of targets and immune cells and obtain significantly differentially expressed core genes (SDECGs). We also selected a machine learning model to screen the feature genes and construct a nomogram. Furthermore, molecular docking, another GEO dataset, and Mendelian randomization (MR) were utilized for preliminary validation. We subsequently clustered the samples based on SDECG expression and consensus clustering and performed analyses between the clusters. Finally, we scored the SDECG score and analyzed the differences between clusters. Results:This study identified 13 SDECGs between DN and normal groups which positively regulated immune cells. We also identified five feature genes (, , , , and ) which were used to construct a nomogram. MR analysis indicated a causal link between elevated IL1B levels and an increased risk of DN. Clustering analysis divided DN samples into four groups, among which, C1 and CI were mainly highly expressed and most immune cells were up-regulated. C2 and CII were the opposite. Finally, we found significant differences in SDECG scores between C1 and C2, CI and CII, respectively. Conclusion:TMJT may alleviate DN via core components (e.g. Denudatin B, hancinol, hirudinoidine A) targeting SDECGs (e.g. SRC, EGF, GAPDH), with the involvement of feature genes and modulation of immune and inflammation-related pathways. These findings have potential implications for clinical practice and future investigations.
10.3389/fendo.2023.1172226
Predicting mortality and re-hospitalization for heart failure: a machine-learning and cluster analysis on frailty and comorbidity.
Aging clinical and experimental research
BACKGROUND:Machine-learning techniques have been recently utilized to predict the probability of unfavorable outcomes among elderly patients suffering from heart failure (HF); yet none has integrated an assessment for frailty and comorbidity. This research seeks to determine which machine-learning-based phenogroups that incorporate frailty and comorbidity are most strongly correlated with death or readmission at hospital for HF within six months following discharge from hospital. METHODS:In this single-center, prospective study of a tertiary care center, we included all patients aged 65 and older discharged for acute decompensated heart failure. Random forest analysis and a Cox multivariable regression were performed to determine the predictors of the composite endpoint. By k-means and hierarchical clustering, those predictors were utilized to phenomapping the cohort in four different clusters. RESULTS:A total of 571 patients were included in the study. Cluster analysis identified four different clusters according to frailty, burden of comorbidities and BNP. As compared with Cluster 4, we found an increased 6-month risk of poor outcomes patients in Cluster 1 (very frail and comorbid; HR 3.53 [95% CI 2.30-5.39]), Cluster 2 (pre-frail with low levels of BNP; HR 2.59 [95% CI 1.66-4.07], and in Cluster 3 (pre-frail and comorbid with high levels of BNP; HR 3.75 [95% CI 2.25-6.27])). CONCLUSIONS:In older patients discharged for ADHF, the cluster analysis identified four distinct phenotypes according to frailty degree, comorbidity, and BNP levels. Further studies are warranted to validate these phenogroups and to guide an appropriate selection of personalized, model of care.
10.1007/s40520-023-02566-w
Cluster Analysis of Cardiovascular Phenotypes in Patients With Type 2 Diabetes and Established Atherosclerotic Cardiovascular Disease: A Potential Approach to Precision Medicine.
Diabetes care
OBJECTIVE:Phenotypic heterogeneity among patients with type 2 diabetes mellitus (T2DM) and atherosclerotic cardiovascular disease (ASCVD) is ill defined. We used cluster analysis machine-learning algorithms to identify phenotypes among trial participants with T2DM and ASCVD. RESEARCH DESIGN AND METHODS:We used data from the Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS) study (n = 14,671), a cardiovascular outcome safety trial comparing sitagliptin with placebo in patients with T2DM and ASCVD (median follow-up 3.0 years). Cluster analysis using 40 baseline variables was conducted, with associations between clusters and the primary composite outcome (cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for unstable angina) assessed by Cox proportional hazards models. We replicated the results using the Exenatide Study of Cardiovascular Event Lowering (EXSCEL) trial. RESULTS:Four distinct phenotypes were identified: cluster I included Caucasian men with a high prevalence of coronary artery disease; cluster II included Asian patients with a low BMI; cluster III included women with noncoronary ASCVD disease; and cluster IV included patients with heart failure and kidney dysfunction. The primary outcome occurred, respectively, in 11.6%, 8.6%, 10.3%, and 16.8% of patients in clusters I to IV. The crude difference in cardiovascular risk for the highest versus lowest risk cluster (cluster IV vs. II) was statistically significant (hazard ratio 2.74 [95% CI 2.29-3.29]). Similar phenotypes and outcomes were identified in EXSCEL. CONCLUSIONS:In patients with T2DM and ASCVD, cluster analysis identified four clinically distinct groups. Further cardiovascular phenotyping is warranted to inform patient care and optimize clinical trial designs.
10.2337/dc20-2806
A Machine Learning-Based Classification of Immunogenic Cell Death Regulators and Characterisation of Immune Microenvironment in Acute Ischemic Stroke.
International journal of clinical practice
Immunogenic cell death (ICD) regulators exert a crucial part in quite a few in numerous biological processes. This study aimed to determine the function and diagnostic value of ICD regulators in acute ischemic stroke (AIS). 31 significant ICD regulators were identified from the gene expression omnibus (GEO) database in this work (the combination of the GSE16561 dataset and the GSE37587 dataset in the comparison of non-AIS and AIS patients). The random forest model was applied and 15 potential ICD regulators were screened to forecast the probability of AIS. A nomogram, on the basis of 11 latent ICD regulators, was performed. The resolution curve analysis indicated that patients can gain benefits from the nomogram. The consensus clustering approach was applied, and AIS patients were divided into 2 ICD clusters (cluster A and cluster B) based on the identified key ICD regulatory factors. To quantify the ICD pattern, 181 ICD-related dissimilarly expressed genes (DEGs) were selected for further investigation. The expression levels of NFKB1, NFKB2, and PARP1 were greater in gene cluster A than in gene cluster B. In conclusion, ICD regulators exerted a crucial part in the progress of AIS. The investigation made by us on ICD patterns perhaps informs prospective immunotherapeutic methods for AIS.
10.1155/2023/9930172
Identification of heterogeneous subtypes and a prognostic model for gliomas based on mitochondrial dysfunction and oxidative stress-related genes.
Frontiers in immunology
Objective:Mitochondrial dysfunction and oxidative stress are known to involved in tumor occurrence and progression. This study aimed to explore the molecular subtypes of lower-grade gliomas (LGGs) based on oxidative stress-related and mitochondrial-related genes (OMRGs) and construct a prognostic model for predicting prognosis and therapeutic response in LGG patients. Methods:A total of 223 OMRGs were identified by the overlap of oxidative stress-related genes (ORGs) and mitochondrial-related genes (MRGs). Using consensus clustering analysis, we identified molecular subtypes of LGG samples from TCGA database and confirmed the differentially expressed genes (DEGs) between clusters. We constructed a risk score model using LASSO regression and analyzed the immune-related profiles and drug sensitivity of different risk groups. The prognostic role of the risk score was confirmed using Cox regression and Kaplan-Meier curves, and a nomogram model was constructed to predict OS rates. We validated the prognostic role of OMRG-related risk score in three external datasets. Quantitative real-time PCR (qRT-PCR) and immunohistochemistry (IHC) staining confirmed the expression of selected genes. Furthermore, wound healing and transwell assays were performed to confirm the gene function in glioma. Results:We identified two OMRG-related clusters and cluster 1 was significantly associated with poor outcomes (P<0.001). The mutant frequencies of IDH were significantly lower in cluster 1 (P<0.05). We found that the OMRG-related risk scores were significantly correlated to the levels of immune infiltration and immune checkpoint expression. High-risk samples were more sensitive to most chemotherapeutic agents. We identified the prognostic role of OMRG-related risk score in LGG patients (HR=2.665, 95%CI=1.626-4.369, P<0.001) and observed that patients with high-risk scores were significantly associated with poor prognosis (P<0.001). We validated our findings in three external datasets. The results of qRT-PCR and IHC staining verified the expression levels of the selected genes. The functional experiments showed a significant decrease in the migration of glioma after knockdown of SCNN1B. Conclusion:We identified two molecular subtypes and constructed a prognostic model, which provided a novel insight into the potential biological function and prognostic significance of mitochondrial dysfunction and oxidative stress in LGG. Our study might help in the development of more precise treatments for gliomas.
10.3389/fimmu.2023.1183475
Network pharmacology-guided and TCM theory-supported and component identification of Naoluoxintong.
Heliyon
Naoluoxintong (NLXT) has been used to treat ischemic stroke (IS) in China for more than two hundred years. However, the pharmacodynamic material basis of NLXT has not been fully studied. Under the guidance of the former network pharmacological analysis, a rapid and reliable method combining UPLC-Q-TOF-MS with the novel informatics UNIFI™ platform was established which was used to study the composition of NLXT and its prototype components and metabolites . A total of 102 compounds were identified. 13 compounds were sourced from "Monarch herb", mainly involving flavonoids and their glycosides. 54 compounds were sourced from "Minister herb", mainly involving triterpenoid saponins, organic acids and lactones. 11 compounds were from the "Assistant herb", mostly containing citric acid and esters of citric acid. 24 compounds were from the "Guide herb", mostly including flavonoids and their glycosides, organic acids and lactones. Moreover, 24 prototype components and 30 metabolites were detected, and transformation pathways for different types of chemical components were provided. This is a comprehensive report on the identification of major chemical components in NLXT and metabolic components in rats by UPLC-Q-TOF-MS combined with UNIFI platform under the guidance of network pharmacology, which is helpful for the quality control of NLXT and the study of quality markers.
10.1016/j.heliyon.2023.e19369
Herb-drug interaction of Xingnaojing injection and Edaravone via pharmacokinetics, mixed inhibition of UGTs, and molecular docking.
Phytomedicine : international journal of phytotherapy and phytopharmacology
BACKGROUND:Xingnaojing injection (XNJ) is a famous emergency Traditional Chinese medicine (TCM) derived from the classical Chinese prescription named An-Gong-Niu-Huang Pill. XNJ is often used along with Edaravone injection (EDA) to treat acute ischemic stroke, they have a synergistic effect in improving patients' blood coagulation and neurological function. However, this combination also causes herb-drug interactions (HDIs), raising the risk of adverse reactions. At present, little is known about the pharmacokinetics and potential mechanism of XNJ combined with EDA. PURPOSE:This study investigates the pharmacokinetics and potential mechanism of the HDIs between XNJ and EDA. STUDY DESIGN AND METHODS:The pharmacokinetic interactions between XNJ and EDA were studied by GC-MS in rats, and the inhibition of XNJ and (-)-borneol on UDP-glucuronosyltransferase (UGTs) were assayed by LC-MS/MS in vitro. In vitro-in vivo extrapolation (IVIVE) and molecular docking were performed to reveal the potential for HDIs. RESULTS:The AUC of (-)-borneol was increased by 1.25-fold in group EDA+XNJ 10 min later, and the C of edaravone was increased by 1.6-fold in group XNJ+EDA 10 min later (p < 0.05). XNJ and (-)-borneol inhibited UGTs-mediated edaravone metabolism in HLM and RLM with a similar inhibitory intensity, in which both of them have stronger inhibition in RLM. These findings demonstrated that (-)-borneol in XNJ mainly exerted UGTs inhibition, which was consistent with the pharmacokinetic assays. (-)-Borneol moderately inhibited UGT2B7 and UGT1A6 by a mixed inhibition mechanism, with K values of 101.393 and 136.217 μM, respectively. Due to the blood concentration of injection was dramatically increased, the HDIs caused by the inhibitory effect of XNJ on UGTs should be highly emphasized. The binding energies of (-)-borneol and edaravone toward UGT2B7 were -6.254 and -6.643 kcal/mol, and the scores towards UGT1A6 were -5.220 and -6.469 kcal/mol, respectively. Moreover, (-)-borneol has similar free energies to many drugs metabolized by UGT2B7 and UGT1A6. CONCLUSIONS:(-)-Borneol modulates the pharmacokinetic behavior of edaravone via mixed inhibition of UGT2B7 and UGT1A6. It provides a theoretical basis for the synergistic effect of XNJ and EDA combinations in clinical practice. When XNJ is used along with UGT2B7 and UGT1A6 substrates, it should be used clinically with caution.
10.1016/j.phymed.2023.154696
Single cell sequencing revealed the mechanism of CRYAB in glioma and its diagnostic and prognostic value.
Frontiers in immunology
Background:We explored the characteristics of single-cell differentiation data in glioblastoma and established prognostic markers based on CRYAB to predict the prognosis of glioblastoma patients. Aberrant expression of CRYAB is associated with invasive behavior in various tumors, including glioblastoma. However, the specific role and mechanisms of CRYAB in glioblastoma are still unclear. Methods:We assessed RNA-seq and microarray data from TCGA and GEO databases, combined with scRNA-seq data on glioma patients from GEO. Utilizing the Seurat R package, we identified distinct survival-related gene clusters in the scRNA-seq data. Prognostic pivotal genes were discovered through single-factor Cox analysis, and a prognostic model was established using LASSO and stepwise regression algorithms. Moreover, we investigated the predictive potential of these genes in the immune microenvironment and their applicability in immunotherapy. Finally, experiments confirmed the functional significance of the high-risk gene CRYAB. Results:By analyzing the ScRNA-seq data, we identified 28 cell clusters representing seven cell types. After dimensionality reduction and clustering analysis, we obtained four subpopulations within the oligodendrocyte lineage based on their differentiation trajectory. Using CRYAB as a marker gene for the terminal-stage subpopulation, we found that its expression was associated with poor prognosis. experiments demonstrated that knocking out CRYAB in U87 and LN229 cells reduced cell viability, proliferation, and invasiveness. Conclusion:The risk model based on CRYAB holds promise in accurately predicting glioblastoma. A comprehensive study of the specific mechanisms of CRYAB in glioblastoma would contribute to understanding its response to immunotherapy. Targeting the CRYAB gene may be beneficial for glioblastoma patients.
10.3389/fimmu.2023.1336187
Identification of an immune subtype-related prognostic signature of clear cell renal cell carcinoma based on single-cell sequencing analysis.
Frontiers in oncology
Background:There is growing evidence that immune cells are strongly associated with the prognosis and treatment of clear cell renal cell carcinoma (ccRCC). Our aim is to construct an immune subtype-related model to predict the prognosis of ccRCC patients and to provide guidance for finding appropriate treatment strategies. Methods:Based on single-cell analysis of the GSE152938 dataset from the GEO database, we defined the immune subtype-related genes in ccRCC. Immediately afterwards, we used Cox regression and Lasso regression to build a prognostic model based on TCGA database. Then, we carried out a series of evaluation analyses around the model. Finally, we proved the role of VMP1 in ccRCC by cellular assays. Result:Initially, based on TCGA ccRCC patient data and GEO ccRCC single-cell data, we successfully constructed a prognostic model consisting of five genes. Survival analysis showed that the higher the risk score, the worse the prognosis. We also found that the model had high predictive accuracy for patient prognosis through ROC analysis. In addition, we found that patients in the high-risk group had stronger immune cell infiltration and higher levels of immune checkpoint gene expression. Finally, cellular experiments demonstrated that when the VMP1 gene was knocked down, 786-O cells showed reduced proliferation, migration, and invasion ability and increased levels of apoptosis. Conclusion:Our study can provide a reference for the diagnosis and treatment of patients with ccRCC.
10.3389/fonc.2023.1067987
PLK1 regulating chemoradiotherapy sensitivity of esophageal squamous cell carcinoma through pentose phosphate pathway/ferroptosis.
Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
BACKGROUND:Esophageal squamous cell carcinoma (ESCC) is the most common pathological type of esophageal cancer in China, accounting for more than 90 %. Most patients were diagnosed with advanced-stage ESCC, for whom new adjuvant therapy is recommended. Therefore, it is urgent to explore new therapeutic targets for ESCC. Ferroptosis, a newly discovered iron-dependent programmed cell death, has been shown to play an important role in carcinogenesis by many studies. This study explored the effect of Polo like kinase 1 (PLK1) on chemoradiotherapy sensitivity of ESCC through ferroptosis METHODS: In this study, we knocked out the expression of PLK1 (PLK1-KO) in ESCC cell lines (KYSE150 and ECA109) with CRISPR/CAS9. The effects of PLK1-knock out on G6PD, the rate-limiting enzyme of pentose phosphate pathway (PPP), and downstream NADPH and GSH were explored. The lipid peroxidation was observed by flow cytometry, and the changes in mitochondria were observed by transmission electron microscopy. Next, through the CCK-8 assay and clone formation assay, the sensitivity to cobalt 60 rays, paclitaxel, and cisplatin were assessed after PLK1-knock out, and the nude mouse tumorigenesis experiment further verified it. The regulation of transcription factor YY1 on PLK1 was evaluated by dual luciferase reporter assay. The expression and correlation of PLK1 and YY1, and their impact on prognosis were analyzed in more than 300 ESCC cases from the GEO database and our center. Finally, the above results were further proved by single-cell sequencing. RESULTS:After PLK1 knockout, the expression of G6PD dimer and the level of NADPH and GSH in KYSE150 and ECA109 cells significantly decreased. Accordingly, lipid peroxidation increased, mitochondria became smaller, membrane density increased, and ferroptosis was more likely to occur. However, with the stimulation of exogenous GSH (10 mM), there was no significant difference in lipid peroxidation and ferroptosis between the PLK1-KO group and the control group. After ionizing radiation, the PLK1-KO group had higher lipid peroxidation ratio, more cell death, and was more sensitive to radiation, while exogenous GSH (10 mM) could eliminate this difference. Similar results could also be observed when receiving paclitaxel combined with cisplatin and chemoradiotherapy. The expression of PLK1, G6PD dimer, and the level of NADPH and GSH in KYSE150, ECA109, and 293 T cells stably transfected with YY1-shRNAs significantly decreased, and the cells were more sensitive to radiotherapy and chemotherapy. ESCC patients from the GEO database and our center, YY1 and PLK1 expression were significantly positively-correlated, and the survival of patients with high expression of PLK1 was significantly shorter. Further analysis of single-cell sequencing specimens of ESCC in our center confirmed the above results. CONCLUSION:In ESCC, down-regulation of PLK1 can inhibit PPP, and reduce the level of NADPH and GSH, thereby promoting ferroptosis and improving their sensitivity to radiotherapy and chemotherapy. Transcription factor YY1 has a positive regulatory effect on PLK1, and their expressions were positively correlated. PLK1 may be a target for predicting and enhancing the chemoradiotherapy sensitivity of ESCC.
10.1016/j.biopha.2023.115711
Integrating network pharmacology deciphers the action mechanism of Zuojin capsule in suppressing colorectal cancer.
Fan Jin-Hua,Xu Min-Min,Zhou Li-Ming,Gui Zheng-Wei,Huang Lu,Li Xue-Gang,Ye Xiao-Li
Phytomedicine : international journal of phytotherapy and phytopharmacology
BACKGROUND AND PURPOSE:Zuojin capsule (ZJC), a classical prescription, is outstanding in improving the conditions of patients with gastrointestinal diseases and colorectal cancer (CRC). Although ZJC has multi-ingredient and multi-target characteristics, its pharmacological effect on colorectal cancer and the underlying mechanism remain unclear. METHOD:Here, the activity of ZJC against CRC was evaluated by the experiments with CRC cells and HCT-116 xenografted mice. The key genes of CRC were obtained from the cancer genome atlas (TCGA). The genes potentially targeted by ZJC were collected from traditional Chinese medicine systems pharmacology (TCMSP) database. The underlying pathways related to selected targets were analyzed through gene ontology (GO) and pathway enrichment analyses. Western blot (WB), cellular thermal shift assay (CETSA), molecular docking and quantitative real-time PCR (QRT-PCR) were carried out to confirm the validity of the targets. RESULTS:In vitro and in vivo results indicated that ZJC may inhibit CRC cells and tumor growth. The network pharmacological analysis indicated that 22 compounds, 51 targets and 20 pathways were involved in the compound-target-pathway network. Our results confirmed that ZJC inhibited cycle progression, migration and induced apoptosis by targeting candidate genes (CDKN1A, Bcl2, E2F1, PRKCB, MYC, CDK2, and MMP9). We found that ZJC could directly change the protein level by regulating the protein stability and transcriptional activity of the target. CONCLUSIONS:In summary, combined network pharmacology and biological experiments proved that the main ingredients of ZJC such as quercetin, (R)-Canadine, palmatine, rutaecarpine, evodiamine, beta-sitosterol and berberine can target CDKN1A, Bcl2, E2F1, PRKCB, MYC, CDK2 and MMP9 to combat colorectal cancer. The results of this study provide a basic theory for the clinical trials of Zuojin Capsules against colorectal cancer.
10.1016/j.phymed.2021.153881
Screening of Therapeutic Candidate Genes of Quercetin for Cervical Cancer and Analysis of Their Regulatory Network.
Li Yuanyuan,Kou Jiushe,Wu Tao,Zheng Pengsheng,Chao Xu
OncoTargets and therapy
Purpose:To explore the therapeutic targets and regulatory mechanisms of the antitumor drug quercetin in the treatment of cervical cancer. Methods:Cervical cancer (HeLa) cells were treated with quercetin and subjected to RNA sequencing using the BGISEQ-500 platform. By combined analysis of GEO database and RNA-seq results, the differentially expressed genes (DEGs) (namely, the genes in the GEO database that were upregulated/downregulated in cervical cancer compared with normal cervix and downregulated/upregulated after quercetin treatment) were identified. Functional enrichment and protein-protein interaction analyses were carried out for the DEGs. The candidate genes were identified using the Gentiscape2.2 and MCODE plug-ins for Cytoscape software, and the upstream miRNAs, lncRNAs, and circRNAs of the candidate genes were predicted using the online tools MirDIP, TarBase, and ENCORI. Finally, the regulatory network was constructed using Cytoscape software. Results:Quercetin significantly inhibited the proliferation of cervical cancer cells. The combined analyses of the GEO database and RNA-seq results obtained 74 DEGs, and the functional enrichment analysis of the DEGs identified 861 biological processes, 32 cellular components, 50 molecular functions, and 56 KEGG pathways. Five therapeutic candidate genes, including EGFR, JUN, AR, CD44, and MUC1, were selected, and 10 miRNAs, 1 lncRNA, and 71 circRNAs upstream of these genes were identified. Finally, a lncRNA/circRNA-miRNA-mRNA-pathway regulatory network was constructed. Conclusion:In this study, data mining was used to identify candidate genes and their regulatory network for the treatment of cervical cancer to provide a theoretical basis for targeted therapy of cervical cancer.
10.2147/OTT.S287633
Network pharmacology and bioinformatics were used to construct a prognostic model and immunoassay of core target genes in the combination of quercetin and kaempferol in the treatment of colorectal cancer.
Journal of Cancer
CRC is a malignant tumor seriously threatening human health. Quercetin and kaempferol are representative components of traditional Chinese medicine (TCM). Previous studies have shown that both quercetin and kaempferol have antitumor pharmacological effects, nevertheless, the underlying mechanism of action remains unclear. To explore the synergy and mechanism of quercetin and kaempferol in colorectal cancer. In this study, network pharmacology, and bioinformatics are used to obtain the intersection of drug targets and disease genes. Training gene sets were acquired from the TCGA database, acquired prognostic-related genes by univariate Cox, multivariate Cox, and Lasso-Cox regression models, and validated in the GEO dataset. We also made predictions of the immune function of the samples and used molecular docking to map a model for binding two components to prognostic genes. Through Lasso-Cox regression analysis, we obtained three models of drug target genes. This model predicts the combined role of quercetin and kaempferol in the treatment and prognosis of CRC. Prognostic genes are correlated with immune checkpoints and immune infiltration and play an adjuvant role in the immunotherapy of CRC. Core genes are regulated by quercetin and kaempferol to improve the patient's immune system and thus assist in the treatment of CRC.
10.7150/jca.85517
Comparison of ischemic stroke diagnosis models based on machine learning.
Frontiers in neurology
Background:The incidence, prevalence, and mortality of ischemic stroke (IS) continue to rise, resulting in a serious global disease burden. The prediction models have a great value in the early prediction and diagnosis of IS. Methods:The R software was used to screen the differentially expressed genes (DEGs) of IS and control samples in the datasets GSE16561, GSE58294, and GSE37587 and analyze DEGs for enrichment analysis. The feature genes of IS were obtained by several machine learning algorithms, including the least absolute shrinkage and selector operation (LASSO) logistic regression, the support vector machine-recursive feature elimination (SVM-RFE), and the Random Forest (RF). The IS diagnostic models were constructed based on transcriptomics by machine learning and artificial neural network (ANN). Results:A total of 69 DEGs, mainly involved in immune and inflammatory responses, were identified. The pathways enriched in the IS group were complement and coagulation cascades, lysosome, PPAR signaling pathway, regulation of autophagy, and toll-like receptor signaling pathway. The feature genes selected by LASSO, SVM-RFE, and RF were 17, 10, and 12, respectively. The area under the curve (AUC) of the LASSO model in the training dataset, GSE22255, and GSE195442 was 0.969, 0.890, and 1.000. The AUC of the SVM-RFE model was 0.957, 0.805, and 1.000, respectively. The AUC of the RF model was 0.947, 0.935, and 1.000, respectively. The models have good sensitivity, specificity, and accuracy. The AUC of the LASSO+ANN, SVM-RFE+ANN, and RF+ANN models was 1.000, 0.995, and 0.997, respectively, in the training dataset. However, the AUC of LASSO+ANN, SVM-RFE+ANN, and RF+ANN models was 0.688, 0.605, and 0.619, respectively, in the GSE22255 dataset. The AUC of the LASSO+ANN and RF+ANN models was 0.740 and 0.630, respectively, in the GSE195442 dataset. In the training dataset, the sensitivity, specificity, and accuracy of the LASSO+ANN model were 1.000, 1.000, and 1.000, respectively; of the SVM-RFE+ANN model were 0.946, 0.982, and 0.964, respectively; and of the RF+ANN model were 0.964, 1.000, and 0.982, respectively. In the test datasets, the sensitivity was very satisfactory; however, the specificity and accuracy were not good. Conclusion:The LASSO, SVM-RFE, and RF models have good prediction abilities. However, the ANN model is efficient at classifying positive samples and is unsuitable at classifying negative samples.
10.3389/fneur.2022.1014346
Identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learning.
Frontiers in immunology
Objective:The decreased stability of atherosclerotic plaques increases the risk of ischemic stroke. However, the specific characteristics of dysregulated immune cells and effective diagnostic biomarkers associated with stability in atherosclerotic plaques are poorly characterized. This research aims to investigate the role of immune cells and explore diagnostic biomarkers in the formation of unstable plaques for the sake of gaining new insights into the underlying molecular mechanisms and providing new perspectives for disease detection and therapy. Method:Using the CIBERSORT method, 22 types of immune cells between stable and unstable carotid atherosclerotic plaques from RNA-sequencing and microarray data in the public GEO database were quantitated. Differentially expressed genes (DEGs) were further calculated and were analyzed for enrichment of GO Biological Process and KEGG pathways. Important cell types and hub genes were screened using machine learning methods including least absolute shrinkage and selection operator (LASSO) regression and random forest. Single-cell RNA sequencing and clinical samples were further used to validate critical cell types and hub genes. Finally, the DGIdb database of gene-drug interaction data was utilized to find possible therapeutic medicines and show how pharmaceuticals, genes, and immune cells interacted. Results:A significant difference in immune cell infiltration was observed between unstable and stable plaques. The proportions of M0, M1, and M2 macrophages were significantly higher and that of CD8 T cells and NK cells were significantly lower in unstable plaques than that in stable plaques. With respect to DEGs, antigen presentation genes (CD74, B2M, and HLA-DRA), inflammation-related genes (MMP9, CTSL, and IFI30), and fatty acid-binding proteins (CD36 and APOE) were elevated in unstable plaques, while the expression of smooth muscle contraction genes (TAGLN, ACAT2, MYH10, and MYH11) was decreased in unstable plaques. M1 macrophages had the highest instability score and contributed to atherosclerotic plaque instability. CD68, PAM, and IGFBP6 genes were identified as the effective diagnostic markers of unstable plaques, which were validated by validation datasets and clinical samples. In addition, insulin, nivolumab, indomethacin, and α-mangostin were predicted to be potential therapeutic agents for unstable plaques. Conclusion:M1 macrophages is an important cause of unstable plaque formation, and CD68, PAM, and IGFBP6 could be used as diagnostic markers to identify unstable plaques effectively.
10.3389/fimmu.2022.956078
Anoikis resistance of small airway epithelium is involved in the progression of chronic obstructive pulmonary disease.
Frontiers in immunology
Background:Anoikis resistance is recognized as a crucial step in the metastasis of cancer cells. Most epithelial tumors are distinguished by the ability of epithelial cells to abscond anoikis when detached from the extracellular matrix. However, no study has investigated the involvement of anoikis in the small airway epithelium (SAE) of chronic obstructive pulmonary disease (COPD). Methods:Anoikis-related genes (ANRGs) exhibiting differential expression in COPD were identified using microarray datasets obtained from the Gene Expression Omnibus (GEO) database. Unsupervised clustering was performed to classify COPD patients into anoikis-related subtypes. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were used to annotate the functions between different subtypes. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were leveraged to identify key molecules. The relative proportion of infiltrating immune cells in the SAE was quantified using the CIBERSORT and ssGSEA computational algorithms, and the correlation between key molecules and immune cell abundance was analyzed. The expression of key molecules in BEAS-2B cells exposed to cigarette smoke extract (CSE) was validated using qRT-PCR. Results:A total of 25 ANRGs exhibited differential expression in the SAE of COPD patients, based on which two subtypes of COPD patients with distinct anoikis patterns were identified. COPD patients with anoikis resistance had more advanced GOLD stages and cigarette consumption. Functional annotations revealed a different immune status between COPD patients with pro-anoikis and anoikis resistance. Tenomodulin (TNMD) and long intergenic non-protein coding RNA 656 (LINC00656) were subsequently identified as key molecules involved in this process, and a close correlation between TNMD and the infiltrating immune cells was observed, such as activated CD4 memory T cells, M1 macrophages, and activated NK cells. Further enrichment analyses clarified the relationship between TNMD and the inflammatory and apoptotic signaling pathway as the potential mechanism for regulating anoikis. experiments showed a dramatic upregulation of TNMD and LINC00656 in BEAS-2B cells when exposed to 3% CSE for 48 hours. Conclusion:TNMD contributes to the progression of COPD by inducing anoikis resistance in SAE, which is intimately associated with the immune microenvironment.
10.3389/fimmu.2023.1155478
The anoikis-related gene signature predicts survival accurately in colon adenocarcinoma.
Scientific reports
Colon adenocarcinoma (COAD) is a serious public health problem, the third most common cancer and the second most deadly cancer in the world. About 9.4% of cancer-related deaths in 2020 were due to COAD. Anoikis is a specialized form of programmed cell death that plays an important role in tumor invasion and metastasis. The presence of anti-anoikis factors is associated with tumor aggressiveness and drug resistance. Various bioinformatic methods, such as differential expression analysis, and functional annotation analysis, machine learning, were used in this study. RNA-sequencing and clinical data from COAD patients were obtained from the Gene expression omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Construction of a prognostic nomogram for predicting overall survival (OS) using multivariate analysis and Lasso-Cox regression. Immunohistochemistry (IHC) was our method of validating the expression of seven genes that are linked to anoikis in COAD. We identified seven anoikis-related genes as predictors of COAD survival and prognosis, and confirmed their accuracy in predicting colon adenocarcinoma prognosis by KM survival curves and ROC curves. A seven-gene risk score consisting of NAT1, CDC25C, ATP2A3, MMP3, EEF1A2, PBK, and TIMP1 showed strong prognostic value. Meanwhile, we made a nomogram to predict the survival rate of COAD patients. The immune infiltration assay showed T cells. CD4 memory. Rest and macrophages. M0 has a higher proportion in COAD, and 11 genes related to tumor immunity are important. GDSC2-based drug susceptibility analysis showed that 6 out of 198 drugs were significant in COAD. Anoikis-related genes have potential value in predicting the prognosis of COAD and provide clues for developing new therapeutic strategies for COAD. Immune infiltration and drug susceptibility results provide important clues for finding new personalized treatment options for COAD. These findings also suggest possible mechanisms that may affect prognosis. These results are the starting point for planning individualized treatment and managing patient outcomes.
10.1038/s41598-023-40907-x
An anoikis-related gene signature predicts prognosis and reveals immune infiltration in hepatocellular carcinoma.
Frontiers in oncology
Background:Hepatocellular carcinoma (HCC) is a global health burden with poor prognosis. Anoikis, a novel programmed cell death, has a close interaction with metastasis and progression of cancer. In this study, we aimed to construct a novel bioinformatics model for evaluating the prognosis of HCC based on anoikis-related gene signatures as well as exploring the potential mechanisms. Materials and methods:We downloaded the RNA expression profiles and clinical data of liver hepatocellular carcinoma from TCGA database, ICGC database and GEO database. DEG analysis was performed using TCGA and verified in the GEO database. The anoikis-related risk score was developed univariate Cox regression, LASSO Cox regression and multivariate Cox regression, which was then used to categorize patients into high- and low-risk groups. Then GO and KEGG enrichment analyses were performed to investigate the function between the two groups. CIBERSORT was used for determining the fractions of 22 immune cell types, while the ssGSEA analyses was used to estimate the differential immune cell infiltrations and related pathways. The "pRRophetic" R package was applied to predict the sensitivity of administering chemotherapeutic and targeted drugs. Results:A total of 49 anoikis-related DEGs in HCC were detected and 3 genes (EZH2, KIF18A and NQO1) were selected out to build a prognostic model. Furthermore, GO and KEGG functional enrichment analyses indicated that the difference in overall survival between risk groups was closely related to cell cycle pathway. Notably, further analyses found the frequency of tumor mutations, immune infiltration level and expression of immune checkpoints were significantly different between the two risk groups, and the results of the immunotherapy cohort showed that patients in the high-risk group have a better immune response. Additionally, the high-risk group was found to have higher sensitivity to 5-fluorouracil, doxorubicin and gemcitabine. Conclusion:The novel signature of 3 anoikis-related genes (EZH2, KIF18A and NQO1) can predict the prognosis of patients with HCC, and provide a revealing insight into personalized treatments in HCC.
10.3389/fonc.2023.1158605
Identification of immunological characterization and Anoikis-related molecular clusters in rheumatoid arthritis.
Frontiers in molecular biosciences
To investigate the potential association between Anoikis-related genes, which are responsible for preventing abnormal cellular proliferation, and rheumatoid arthritis (RA). Datasets GSE89408, GSE198520, and GSE97165 were obtained from the GEO with 282 RA patients and 28 healthy controls. We performed differential analysis of all genes and genes. We performed a protein-protein interaction network analysis and identified hub genes based on and cytoscape. Consistent clustering was performed with subgrouping of the disease. SsGSEA were used to calculate immune cell infiltration. Spearman's correlation analysis was employed to identify correlations. Enrichment scores of the GO and KEGG were calculated with the ssGSEA algorithm. The WGCNA and the database were used to mine hub genes' interactions with drugs. There were 26 differentially expressed Anoikis-related genes ( = 0.05, log2FC = 1) and HLA genes exhibited differential expression ( < 0.05) between the disease and control groups. Protein-protein interaction was observed among differentially expressed genes, and the correlation between and was found to be the highest; There were significant differences in the degree of immune cell infiltration between most of the immune cell types in the disease group and normal controls ( < 0.05). Anoikis-related genes were highly correlated with HLA genes. Based on the expression of Anoikis-related genes, RA patients were divided into two disease subtypes (cluster1 and cluster2). There were 59 differentially expressed Anoikis-related genes found, which exhibited significant differences in functional enrichment, immune cell infiltration degree, and gene expression ( < 0.05). Cluster2 had significantly higher levels in all aspects than cluster1 did. The co-expression network analysis showed that cluster1 had 51 hub differentially expressed genes and cluster2 had 72 hub differentially expressed genes. Among them, three hub genes of cluster1 were interconnected with 187 drugs, and five hub genes of cluster2 were interconnected with 57 drugs. Our study identified a link between Anoikis-related genes and RA, and two distinct subtypes of RA were determined based on Anoikis-related gene expression. Notably, cluster2 may represent a more severe state of RA.
10.3389/fmolb.2023.1202371
ATRX is a predictive marker for endocrinotherapy and chemotherapy resistance in HER2-/HR+ breast cancer through the regulation of the AR, GLI3 and GATA2 transcriptional network.
Aging
Drug resistance in breast cancer (BC) is a clinical challenge. Exploring the mechanism and identifying a precise predictive biomarker for the drug resistance in BC is critical. Three first-line drug (paclitaxel, doxorubicin and tamoxifen) resistance datasets in BC from GEO were merged to obtain 1,461 differentially expressed genes for weighted correlation network analysis, resulting in identifying ATRX as the hub gene. ATRX is a chromatin remodelling protein, therefore, ATRX-associated transcription factors were explored, thereby identifying the network of AR, GLI3 and GATA2. GO and KEGG analyses revealed immunity, transcriptional regulation and endocrinotherapy/chemotherapy resistance were enriched. Moreover, CIBERSORT revealed immunity regulation was inhibited in the resistance group. ssGSEA showed a significantly lower immune status in the ATRX-Low group compared to the ATRX-High group. Furthermore, the peaks of H3K9me3 ChIP-seq on the four genes were higher in normal tissues than in BC tissues. Notably, the frequency of ATRX mutation was higher than BRCA in BC. Moreover, depressed ATRX revealed worse overall survival and disease-free survival in the human epidermal growth factor receptor 2 (HER2)-/hormone receptor (HR)+ BC. Additionally, depressed ATRX predicted poor results for patients who underwent endocrinotherapy or chemotherapy in the HER2-/HR+ BC subgroup. A nomogram based on ATRX, TILs and ER exhibited a significantly accurate survival prediction ability. Importantly, overexpression of ATRX significantly inhibited the IC of the three first-line drugs on MCF-7 cell. Thus, ATRX is an efficient predictive biomarker for endocrinotherapy and chemotherapy resistance in HER2-/HR+ BC and acts by suppressing the AR, GLI3 and GATA2 transcriptional network.
10.18632/aging.205327
Identification of Key Genes and Pathways Associated With Paclitaxel Resistance in Esophageal Squamous Cell Carcinoma Based on Bioinformatics Analysis.
Shen Zhimin,Chen Mingduan,Luo Fei,Xu Hui,Zhang Peipei,Lin Jihong,Kang Mingqiang
Frontiers in genetics
Esophageal squamous cell carcinoma (ESCC) ranks as the fourth leading cause of cancer-related death in China. Although paclitaxel has been shown to be effective in treating ESCC, the prolonged use of this chemical will lead to paclitaxel resistance. In order to uncover genes and pathways driving paclitaxel resistance in the progression of ESCC, bioinformatics analyses were performed based on The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database including GSE86099 and GSE161533. Differential expression analysis was performed in TCGA data and two GEO datasets to obtain differentially expressed genes (DEGs). Based on GSE161533, weighted gene co-expression network analysis (WGCNA) was conducted to identify the key modules associated with ESCC tumor status. The DEGs common to the two GEO datasets and the genes in the key modules were intersected to obtain the paclitaxel resistance-specific or non-paclitaxel resistance-specific genes, which were subjected to subsequent least absolute shrinkage and selection operator (LASSO) feature selection, whereby paclitaxel resistance-specific or non-paclitaxel resistance-specific key genes were selected. Ten machine learning models were used to validate the biological significance of these key genes; the potential therapeutic drugs for paclitaxel resistance-specific genes were also predicted. As a result, we identified 24 paclitaxel resistance-specific genes and 18 non-paclitaxel resistance-specific genes. The ESCC machine classifiers based on the key genes achieved a relatively high AUC value in the cross-validation and in an independent test set, GSE164158. A total of 207 drugs (such as bevacizumab) were predicted to be alternative therapeutics for ESCC patients with paclitaxel resistance. These results might shed light on the in-depth research of paclitaxel resistance in the context of ESCC progression.
10.3389/fgene.2021.671639
TJP3 promotes T cell immunity escape and chemoresistance in breast cancer: a comprehensive analysis of anoikis-based prognosis prediction and drug sensitivity stratification.
Aging
BACKGROUND:Overcoming anoikis is a necessity during the metastasis and invasion of tumors. Recently, anoikis has been reported to be involved in tumor immunity and has been used to construct prognosis prediction models. However, the roles of anoikis in regulating tumor immunity and drug sensitivity in breast cancer are still not clear and therefore worth uncovering. METHODS:TCGA and GEO data are the source of gene expression profiles, which are used to identify anoikis-related-gene (ARG)-based subtypes. R4.2 is used for data analysis. RESULTS:Breast cancer is divided into three subgroups, amongst which shows prognosis differences in pan-cancer cohort, ACC, BLCA, BRCA, LUAD, MESO, PAAD, and SKCM. In breast cancer, it shows significant differences in clinical features, immune cell infiltration and drug sensitivity. Machine learning constructs prognosis prediction model, which is useful to perform chemotherapy sensitivity stratification. Following, TJP3 is identified and verified as the key ARG, up-regulation of which increases tolerance of paclitaxel-induced cell toxicity, accompanied with increased expression of caspas3 and cleaved-caspase3. In addition, Down-regulation of TJP3 weakens the cell migration, which accompanied with increased expression of E-cad and decreased expression of vimentin, twist1, zeb1, and MMP7. Furthermore, the expression level of PD-L1 is negative correlated with TJP3. CONCLUSION:ARGs-based subgroup stratification is useful to recognize chemotherapy sensitive cohort, and also is useful to predict clinical outcome. TJP3 promotes chemoresistance, tumor metastasis and potential immunotherapy escape in breast cancer.
10.18632/aging.205208
Ferroptosis-related differentially expressed genes serve as new biomarkers in ischemic stroke and identification of therapeutic drugs.
Frontiers in nutrition
Background:Iron is an essential nutrient element, and iron metabolism is related to many diseases. Ferroptosis is an iron-dependent form of regulated cell death associated with ischemic stroke (IS). Hence, this study intended to discover and validate the possible ferroptosis-related genes involved in IS. Materials and methods:GSE16561, GSE37587, and GSE58294 were retrieved from the GEO database. Using R software, we identified ferroptosis-related differentially expressed genes (DEGs) in IS. Protein-protein interactions (PPIs) and enrichment analyses were conducted. The ROC curve was plotted to explore the diagnostic significance of those identified genes. The consistent clustering method was used to classify the IS samples. The level of immune cell infiltration of different subtypes was evaluated by ssGSEA and CIBERSORT algorithm. Validation was conducted in the test sets GSE37587 and GSE58294. Results:Twenty-one ferroptosis-related DEGs were detected in IS vs. the normal controls. Enrichment analysis shows that the 21 DEGs are involved in monocarboxylic acid metabolism, iron ion response, and ferroptosis. Moreover, their expression levels were pertinent to the age and gender of IS patients. The ROC analysis demonstrated remarkable diagnostic values of LAMP2, TSC22D3, SLC38A1, and RPL8 for IS. Transcription factors and targeting miRNAs of the 21 DEGs were determined. Vandetanib, FERRIC CITRATE, etc., were confirmed as potential therapeutic drugs for IS. Using 11 hub genes, IS patients were categorized into C1 and C2 subtypes. The two subtypes significantly differed between immune cell infiltration, checkpoints, and HLA genes. The 272 DEGs were identified from two subtypes and their biological functions were explored. Verification was performed in the GSE37587 and GSE58294 datasets. Conclusion:Our findings indicate that ferroptosis plays a critical role in the diversity and complexity of the IS immune microenvironment.
10.3389/fnut.2022.1010918
Implications of Circadian Rhythm in Stroke Occurrence: Certainties and Possibilities.
Fodor Dana Marieta,Marta Monica Mihaela,Perju-Dumbravă Lăcrămioara
Brain sciences
Stroke occurrence is not randomly distributed over time but has circadian rhythmicity with the highest frequency of onset in the morning hours. This specific temporal pattern is valid for all subtypes of cerebral infarction and intracerebral hemorrhage. It also correlates with the circadian variation of some exogenous factors such as orthostatic changes, physical activity, sleep-awake cycle, as well as with endogenous factors including dipping patterns of blood pressure, or morning prothrombotic and hypofibrinolytic states with underlying cyclic changes in the autonomous system and humoral activity. Since the internal clock is responsible for these circadian biological changes, its disruption may increase the risk of stroke occurrence and influence neuronal susceptibility to injury and neurorehabilitation. This review aims to summarize the literature data on the circadian variation of cerebrovascular events according to physiological, cellular, and molecular circadian changes, to survey the available information on the chronotherapy and chronoprophylaxis of stroke and its risk factors, as well as to discuss the less reviewed impact of the circadian rhythm in stroke onset on patient outcome and functional status after stroke.
10.3390/brainsci11070865
CRS: a circadian rhythm score model for predicting prognosis and treatment response in cancer patients.
Journal of translational medicine
BACKGROUND:Circadian rhythm regulates complex physiological activities in organisms. A strong link between circadian dysfunction and cancer has been identified. However, the factors of dysregulation and functional significance of circadian rhythm genes in cancer have received little attention. METHODS:In 18 cancer types from The Cancer Genome Atlas (TCGA), the differential expression and genetic variation of 48 circadian rhythm genes (CRGs) were examined. The circadian rhythm score (CRS) model was created using the ssGSEA method, and patients were divided into high and low groups based on the CRS. The Kaplan-Meier curve was created to assess the patient survival rate. Cibersort and estimate methods were used to identify the infiltration characteristics of immune cells between different CRS subgroups. Gene Expression Omnibus (GEO) dataset is used as verification queue and model stability evaluation queue. The CRS model's ability to predict chemotherapy and immunotherapy was assessed. Wilcoxon rank-sum test was used to compare the differences of CRS among different patients. We use CRS to identify potential "clock-drugs" by the connective map method. RESULTS:Transcriptomic and genomic analyses of 48 CRGs revealed that most core clock genes are up-regulated, while clock control genes are down-regulated. Furthermore, we show that copy number variation may affect CRGs aberrations. Based on CRS, patients can be classified into two groups with significant differences in survival and immune cell infiltration. Further studies showed that patients with low CRS were more sensitive to chemotherapy and immunotherapy. Additionally, we identified 10 compounds (e.g. flubendazole, MLN-4924, ingenol) that are positively associated with CRS, and have the potential to modulate circadian rhythms. CONCLUSIONS:CRS can be utilized as a clinical indicator to predict patient prognosis and responsiveness to therapy, and identify potential "clock-drugs".
10.1186/s12967-023-04013-w
Identification of PANoptosis-Based Prognostic Signature for Predicting Efficacy of Immunotherapy and Chemotherapy in Hepatocellular Carcinoma.
Genetics research
Background:PANoptosis has been a research hotspot, but the role of PANoptosis in hepatocellular carcinoma (HCC) remains widely unknown. Drug resistance and low response rate are the main limitations of chemotherapy and immunotherapy in HCC. Thus, construction of a prognostic signature to predict prognosis and recognize ideal patients for corresponding chemotherapy and immunotherapy is necessary. Method:The mRNA expression data of HCC patients was collected from TCGA database. Through LASSO and Cox regression, we developed a prognostic signature based on PANoptosis-related genes. KM analysis and ROC curve were implemented to evaluate the prognostic efficacy of this signature, and ICGC and GEO database were used as external validation cohorts. The immune cell infiltration, immune status, and IC50 of chemotherapeutic drugs were compared among different risk subgroups. The relationships between the signature and the efficacy of ICI therapy, sorafenib treatment, and transcatheter arterial chemoembolization (TACE) therapy were investigated. Result:A 3-gene prognostic signature was constructed which divided the patients into low- and high-risk subgroups. Low-risk patients had better prognosis, and the risk score was proved to be an independent predictor of overall survival (OS), which had a well predictive effect. Patients in high-risk population had more immunosuppressive cells (Tregs, M0 macrophages, and MDSCs), higher TIDE score and TP53 mutation rate, and elevated activity of base excision repair (BER) pathways. Patients with low risk benefited more from ICI, TACE, and sorafenib therapy. The predictive value of the risk score was comparable with TIDE and MSI for OS under ICI therapy. The risk score could be a biomarker to predict the response to ICI, TACE, and sorafenib therapy. Conclusion:The novel signature based on PANoptosis is a promising biomarker to distinguish the prognosis predict the benefit of ICI, TACE, and sorafenib therapy, and forecast the response to them.
10.1155/2023/6879022
Utilizing a novel model of PANoptosis-related genes for enhanced prognosis and immune status prediction in kidney renal clear cell carcinoma.
Apoptosis : an international journal on programmed cell death
Kidney renal clear cell carcinoma (KIRC) is the most common histopathologic type of renal cell carcinoma. PANoptosis, a cell death pathway that involves an interplay between pyroptosis, apoptosis and necroptosis, is associated with cancer immunity and development. However, the prognostic significance of PANoptosis in KIRC remains unclear. RNA-sequencing expression and mutational profiles from 532 KIRC samples and 72 normal samples with sufficient clinical data were retrieved from the Cancer Genome Atlas (TCGA) database. A prognostic model was constructed using differentially expressed genes (DEGs) related to PANoptosis in the TCGA cohort and was validated in a Gene Expression Omnibus (GEO) cohorts. Incorporating various clinical features, the risk model remained an independent prognostic factor in multivariate analysis, and it demonstrated superior performance compared to unsupervised clustering of the 21 PANoptosis-related genes alone. Further mutational analysis showed fewer VHL and more BAP1 alterations in the high-risk group, with alterations in both genes also associated with patient prognosis. The high-risk group was characterized by an unfavorable immune microenvironment, marked by reduced levels of CD4 + T cells and natural killer cells, but increased M2 macrophages and regulatory T cells. Finally, the risk model was predictive of response to immune checkpoint blockade, as well as sensitivity to sunitinib and paclitaxel. The PANoptosis-related risk model developed in this study enables accurate prognostic prediction in KIRC patients. Its associations with the tumor immune microenvironment and drug efficacy may offer potential therapeutic targets and inform clinical decisions.
10.1007/s10495-023-01932-3
Analysis of the role of PANoptosis in seizures via integrated bioinformatics analysis and experimental validation.
Heliyon
Background:Epilepsy is recognized as the most common chronic neurological condition among children, and hippocampal neuronal cell death has been identified as a crucial factor in the pathophysiological processes underlying seizures. In recent studies, PANoptosis, a newly characterized form of cell death, has emerged as a significant contributor to the development of various neurological disorders, including Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. PANoptosis involves the simultaneous activation of pyroptosis, apoptosis, and necroptosis within the same population of cells. However, its specific role in the context of seizures remains to be fully elucidated. Further investigation is required to uncover the precise involvement of PANoptosis in the pathogenesis of seizures and to better understand its potential implications for the development of targeted therapeutic approaches in epilepsy. Methods:In this study, the gene expression data of the hippocampus following the administration of kainic acid (KA) or NaCl was obtained from the Gene Expression Omnibus (GEO) database. The PANoptosis-related gene set was compiled from the GeneCards database and previous literature. Time series analysis was performed to analyze the temporal expression patterns of the PANoptosis-related genes. Gene set variation analysis (GSVA), Gene ontology (GO), and Kyoto encyclopedia of genes and genomes (KEGG) were employed to explore potential biological mechanisms underlying PANoptosis and its role in seizures. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were utilized to identify pivotal gene modules and PANoptosis-related genes associated with the pathophysiological processes underlying seizures. To validate the expression of PANoptosis-related genes, Western blotting or quantitative real-time polymerase chain reaction (qRT-PCR) assays were conducted. These experimental validations were performed in human blood samples, animal models, and cell models to verify the expression patterns of the PANoptosis-related genes and their relevance to epilepsy. Results:The GSVA analysis performed in this study demonstrated that PANoptosis-related genes have the potential to distinguish between the control group and KA-induced epileptic mice. This suggests that the expression patterns of these genes are significantly altered in response to KA-induced epilepsy. Furthermore, the Weighted gene co-expression network analysis (WGCNA) identified the blue module as being highly associated with epileptic phenotypes. This module consists of genes that exhibit correlated expression patterns specifically related to epilepsy. Within the blue module, 10 genes were further identified as biomarker genes for epilepsy. These genes include MLKL, IRF1, RIPK1, GSDMD, CASP1, CASP8, ZBP1, CASP6, PYCARD, and IL18. These genes likely play critical roles in the pathophysiology of epilepsy and could serve as potential biomarkers for diagnosing or monitoring the condition. Conclusion:In conclusion, our study suggests that the hippocampal neuronal cell death in epilepsy may be closely related to PANoptosis, a novel form of cell death, which provides insights into the underlying pathophysiological processes of epilepsy and helps the development of novel therapeutic approaches for epilepsy.
10.1016/j.heliyon.2024.e26219
ITGAM is a critical gene in ischemic stroke.
Aging
BACKGROUND:Globally, ischemic stroke (IS) is ranked as the second most prevailing cause of mortality and is considered lethal to human health. This study aimed to identify genes and pathways involved in the onset and progression of IS. METHODS:GSE16561 and GSE22255 were downloaded from the Gene Expression Omnibus (GEO) database, merged, and subjected to batch effect removal using the ComBat method. The limma package was employed to identify the differentially expressed genes (DEGs), followed by enrichment analysis and protein-protein interaction (PPI) network construction. Afterward, the cytoHubba plugin was utilized to screen the hub genes. Finally, a ROC curve was generated to investigate the diagnostic value of hub genes. Validation analysis through a series of experiments including qPCR, Western blotting, TUNEL, and flow cytometry was performed. RESULTS:The analysis incorporated 59 IS samples and 44 control samples, revealing 226 DEGs, of which 152 were up-regulated and 74 were down-regulated. These DEGs were revealed to be linked with the inflammatory and immune responses through enrichment analyses. Overall, the ROC analysis revealed the remarkable diagnostic potential of ITGAM and MMP9 for IS. Quantitative assessment of these genes showed significant overexpression in IS patients. ITGAM modulation influenced the secretion of critical inflammatory cytokines, such as IL-1β, IL-6, and TNF-α, and had a distinct impact on neuronal apoptosis. CONCLUSIONS:The inflammation and immune response were identified as potential pathological mechanisms of IS by bioinformatics and experiments. In addition, ITGAM may be considered a potential therapeutic target for IS.
10.18632/aging.205729
Novel diagnostic biomarkers of oxidative stress, ferroptosis, immune infiltration characteristics and experimental validation in ischemic stroke.
Aging
Ischemic stroke (IS) is a prominent type of cerebrovascular disease leading to death and disability in an aging society and is closely related to oxidative stress. Gene expression profiling (GSE222551) was derived from Gene Expression Omnibus (GEO), and 1934 oxidative stress (OS) genes were obtained from the GeneCards database. Subsequently, we identified 149 differentially expressed genes related to OS (DEOSGs). Finally, PTGS2, FOS, and RYR1 were identified as diagnostic markers of IS. Moreover, GSE16561 was used to validate the DEOSGs. Two diagnostic genes (PTGS2 and FOS) were significantly highly expressed, while RYR1 was significantly lowly expressed in the IS group. Remarkably, immune infiltration characteristics of these three genes were analyzed, and we found that PTGS2, FOS, and RYR1 were mainly correlated with Mast cells activated, Neutrophils, and Plasma cells, respectively. Next, we intersected three DEOSGs with the ferroptosis gene set, the findings revealed that only PTGS2 was a differentially expressed gene of ferroptosis. High PTGS2 expression levels in the infarcted cortex of middle cerebral artery occlusion (MCAO) rats were confirmed by immunofluorescence (IF), western blotting (WB), and Immunohistochemistry (IHC). Inhibition of PTGS2 clearly improved the neurological outcome of rats by decreasing infarct volume, neurological problems, and modified neurological severity scores following IS compared with the controls. The protective effect of silencing PTGS2 may be related to anti-oxidative stress and ferroptosis. In conclusion, this work may provide a new perspective for the research of IS, and further research based on PTGS2 may be a breakthrough.
10.18632/aging.205415
Identification of anoikis-related genes classification patterns and immune infiltration characterization in ischemic stroke based on machine learning.
Frontiers in aging neuroscience
Introduction:Ischemic stroke (IS) is a type of stroke that leads to high mortality and disability. Anoikis is a form of programmed cell death. When cells detach from the correct extracellular matrix, anoikis disrupts integrin junctions, thus preventing abnormal proliferating cells from growing or attaching to an inappropriate matrix. Although there is growing evidence that anoikis regulates the immune response, which makes a great contribution to the development of IS, the role of anoikis in the pathogenesis of IS is rarely explored. Methods:First, we downloaded GSE58294 set and GSE16561 set from the NCBI GEO database. And 35 anoikis-related genes (ARGs) were obtained from GSEA website. The CIBERSORT algorithm was used to estimate the relative proportions of 22 infiltrating immune cell types. Next, consensus clustering method was used to classify ischemic stroke samples. In addition, we used least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms to screen the key ARGs in ischemic stroke. Next, we performed receiver operating characteristics (ROC) analysis to assess the accuracy of each diagnostic gene. At the same time, the nomogram was constructed to diagnose IS by integrating trait genes. Then, we analyzed the correlation between gene expression and immune cell infiltration of the diagnostic genes in the combined database. And gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analysis were performed on these genes to explore differential signaling pathways and potential functions, as well as the construction and visualization of regulatory networks using NetworkAnalyst and Cytoscape. Finally, we investigated the expression pattern of ARGs in IS patients across age or gender. Results:Our study comprehensively analyzed the role of ARGs in IS for the first time. We revealed the expression profile of ARGs in IS and the correlation with infiltrating immune cells. And The results of consensus clustering analysis suggested that we can classify IS patients into two clusters. The machine learning analysis screened five signature genes, including AKT1, BRMS1, PTRH2, TFDP1 and TLE1. We also constructed nomogram models based on the five risk genes and evaluated the immune infiltration correlation, gene-miRNA, gene-TF and drug-gene interaction regulatory networks of these signature genes. The expression of ARGs did not differ by sex or age. Discussion:This study may provide a beneficial reference for further elucidating the pathogenesis of IS, and render new ideas for drug screening, individualized therapy and immunotherapy of IS.
10.3389/fnagi.2023.1142163
miR-339 Promotes Hypoxia-Induced Neuronal Apoptosis and Impairs Cell Viability by Targeting FGF9/CACNG2 and Mediating MAPK Pathway in Ischemic Stroke.
Gao Xiao-Zeng,Ma Ru-Hua,Zhang Zhao-Xia
Frontiers in neurology
Ischemic stroke (IS) is a common cerebrovascular disease characterized by insufficient blood blow to the brain and the second leading cause of death as well as disability worldwide. Recent literatures have indicated that abnormal expression of miR-339 is closely related to IS. In this study, we attempted to assess the biological function of miR-339 and its underlying mechanism in IS. By accessing the GEO repository, the expression of miR-339, FGF9, and CACNG2 in middle cerebral artery occlusion (MCAO) and non-MCAO was evaluated. PC12 cells after oxygen-glucose deprivation/reoxygenation (OGD/R) treatment were prepared to mimic the IS model. The levels of miR-339, FGF9, CACNG2, and MAPK-related markers were quantitatively measured by qRT-PCR and Western blot. CCK-8 and flow cytometry analyses were performed to examine cell viability and apoptosis, respectively. IS-related potential pathways were identified using KEGG enrichment analysis and GO annotations. Bioinformatics analysis and dual-luciferase reporter assay were used to predict and verify the possible target of miR-339. Our results showed that miR-339 expression was significantly increased in MCAO and OGD/R-treated PC12 cells. Overexpression of miR-339 inhibited cell viability of PC12 cells subjected to OGD/R treatment. FGF9 and CACMG2 are direct targets of miR-339 and can reverse the aggressive effect of miR-339 on the proliferation and apoptosis of OGD/R-treated PC12 cells. Moreover, miR-339 mediated the activation of the MAPK pathway, which was inhibited by the FGF9/CACNG2 axis in PC12 cells treated by OGD/R stimulation. In summary, these findings suggested that miR-339 might act as a disruptive molecule to accelerate the IS progression via targeting the FGF9/CACNG2 axis and mediating the MAPK pathway.
10.3389/fneur.2020.00436
Identification of hypoxia-related genes and exploration of their relationship with immune cells in ischemic stroke.
Scientific reports
Ischemic stroke (IS) is a major threat to human health, and it is the second leading cause of long-term disability and death in the world. Impaired cerebral perfusion leads to acute hypoxia and glucose deficiency, which in turn induces a stroke cascade response that ultimately leads to cell death. Screening and identifying hypoxia-related genes (HRGs) and therapeutic targets is important for neuroprotection before and during brain recanalization to protect against injury and extend the time window to further improve functional outcomes before pharmacological and mechanical thrombolysis. First, we downloaded the GSE16561 and GSE58294 datasets from the NCBI GEO database. Bioinformatics analysis of the GSE16561 dataset using the limma package identified differentially expressed genes (DEGs) in ischemic stroke using adj. p. values < 0.05 and a fold change of 0.5 as thresholds. The Molecular Signature database and Genecards database were pooled to obtain hypoxia-related genes. 19 HRGs associated with ischemic stroke were obtained after taking the intersection. LASSO regression and multivariate logistic regression were applied to identify critical biomarkers with independent diagnostic values. ROC curves were constructed to validate their diagnostic efficacy. We used CIBERSORT to analyze the differences in the immune microenvironment between IS patients and controls. Finally, we investigated the correlation between HRGs and infiltrating immune cells to understand molecular immune mechanisms better. Our study analyzed the role of HRGs in ischemic stroke. Nineteen hypoxia-related genes were obtained. Enrichment analysis showed that 19 HRGs were involved in response to hypoxia, HIF-1 signaling pathway, autophagy, autophagy of mitochondrion, and AMPK signaling pathway. Because of the good diagnostic properties of SLC2A3, we further investigated the function of SLC2A3 and found that it is closely related to immunity. We have also explored the relevance of other critical genes to immune cells. Our findings suggest that hypoxia-related genes play a crucial role in the diversity and complexity of the IS immune microenvironment. Exploring the association between hypoxia-related critical genes and immune cells provides innovative insights into the therapeutic targets for ischemic stroke.
10.1038/s41598-023-37753-2
Re-Exploring the Inflammation-Related Core Genes and Modules in Cerebral Ischemia.
Molecular neurobiology
The genetic transcription profile of brain ischemic and reperfusion injury remains elusive. To address this, we used an integrative analysis approach including differentially expressed gene (DEG) analysis, weighted-gene co-expression network analysis (WGCNA), and pathway and biological process analysis to analyze data from the microarray studies of nine mice and five rats after middle cerebral artery occlusion (MCAO) and six primary cell transcriptional datasets in the Gene Expression Omnibus (GEO). (1) We identified 58 upregulated DEGs with more than 2-fold increase, and adj. p < 0.05 in mouse datasets. Among them, Atf3, Timp1, Cd14, Lgals3, Hmox1, Ccl2, Emp1, Ch25h, Hspb1, Adamts1, Cd44, Icam1, Anxa2, Rgs1, and Vim showed significant increases in both mouse and rat datasets. (2) Ischemic treatment and reperfusion time were the main confounding factors in gene profile changes, while sampling site and ischemic time were not. (3) WGCNA identified a reperfusion-time irrelevant and inflammation-related module and a reperfusion-time relevant and thrombo-inflammation related module. Astrocytes and microglia were the main contributors of the gene changes in these two modules. (4) Forty-four module core hub genes were identified. We validated the expression of unreported stroke-associated core hubs or human stroke-associated core hubs. Zfp36 mRNA was upregulated in permanent MCAO; Rhoj, Nfkbiz, Ms4a6d, Serpina3n, Adamts-1, Lgals3, and Spp1 mRNAs were upregulated in both transient MCAO and permanent MCAO; and NFKBIZ, ZFP3636, and MAFF proteins, unreported core hubs implicated in negative regulation of inflammation, were upregulated in permanent MCAO, but not in transient MCAO. Collectively, these results expand our knowledge of the genetic profile involved in brain ischemia and reperfusion, highlighting the crucial role of inflammatory disequilibrium in brain ischemia.
10.1007/s12035-023-03275-1
Identification of cell death-related biomarkers and immune infiltration in ischemic stroke between male and female patients.
Frontiers in immunology
Background:Stroke is the second leading cause of death and the third leading cause of disability worldwide, with ischemic stroke (IS) being the most prevalent. A substantial number of irreversible brain cell death occur in the short term, leading to impairment or death in IS. Limiting the loss of brain cells is the primary therapy target and a significant clinical issue for IS therapy. Our study aims to establish the gender specificity pattern from immune cell infiltration and four kinds of cell-death perspectives to improve IS diagnosis and therapy. Methods:Combining and standardizing two IS datasets (GSE16561 and GSE22255) from the GEO database, we used the CIBERSORT algorithm to investigate and compare the immune cell infiltration in different groups and genders. Then, ferroptosis-related differently expressed genes (FRDEGs), pyroptosis-related DEGs (PRDEGs), anoikis-related DEGs (ARDEGs), and cuproptosis-related DEGs (CRDEGs) between the IS patient group and the healthy control group were identified in men and women, respectively. Machine learning (ML) was finally used to generate the disease prediction model for cell death-related DEGs (CDRDEGs) and to screen biomarkers related to cell death involved in IS. Results:Significant changes were observed in 4 types of immune cells in male IS patients and 10 types in female IS patients compared with healthy controls. In total, 10 FRDEGs, 11 PRDEGs, 3 ARDEGs, and 1 CRDEG were present in male IS patients, while 6 FRDEGs, 16 PRDEGs, 4 ARDEGs, and 1 CRDEG existed in female IS patients. ML techniques indicated that the best diagnostic model for both male and female patients was the support vector machine (SVM) for CDRDEG genes. SVM's feature importance analysis demonstrated that SLC2A3, MMP9, C5AR1, ACSL1, and NLRP3 were the top five feature-important CDRDEGs in male IS patients. Meanwhile, the PDK4, SCL40A1, FAR1, CD163, and CD96 displayed their overwhelming influence on female IS patients. Conclusion:These findings contribute to a better knowledge of immune cell infiltration and their corresponding molecular mechanisms of cell death and offer distinct clinically relevant biological targets for IS patients of different genders.
10.3389/fimmu.2023.1164742
Glycolysis induces Th2 cell infiltration and significantly affects prognosis and immunotherapy response to lung adenocarcinoma.
Functional & integrative genomics
Glycolysis has a major role in cancer progression and can affect the tumor immune microenvironment, while its specific role in lung adenocarcinoma (LUAD) remains poorly studied. We obtained publicly available data from The Cancer Genome Atlas and Gene Expression Omnibus databases and used R software to analyze the specific role of glycolysis in LUAD. The Single Sample Gene Set Enrichment Analysis (ssGSEA) indicated a correlation between glycolysis and unfavorable clinical outcome, as well as a repression effect on the immunotherapy response of LUAD patients. Pathway enrichment analysis revealed a significant enrichment of MYC targets, epithelial-mesenchymal transition (EMT), hypoxia, G2M checkpoint, and mTORC1 signaling pathways in patients with higher activity of glycolysis. Immune infiltration analysis showed a higher infiltration of M0 and M1 macrophages in patients with elevated activity of glycolysis. Moreover, we developed a prognosis model based on six glycolysis-related genes, including DLGAP5, TOP2A, KIF20A, OIP5, HJURP, and ANLN. Both the training and validation cohorts demonstrated the high efficiency of prognostic prediction in this model, which identified that patients with high risk may have a poorer prognosis and lower sensitivity to immunotherapy. Additionally, we also found that Th2 cell infiltration may predict poorer survival and resistance to immunotherapy. The study indicated that glycolysis is significantly associated with poor prognosis in patients with LUAD and immunotherapy resistance, which might be partly dependent on the Th2 cell infiltration. Additionally, the signature comprised of six genes related to glycolysis showed promising predictive value for LUAD prognosis.
10.1007/s10142-023-01155-4
Identification of potential biological processes and key genes in diabetes-related stroke through weighted gene co-expression network analysis.
BMC medical genomics
BACKGROUND:Type 2 diabetes mellitus (T2DM) is an established risk factor for acute ischemic stroke (AIS). Although there are reports on the correlation of diabetes and stroke, data on its pathogenesis is limited. This study aimed to explore the underlying biological mechanisms and promising intervention targets of diabetes-related stroke. METHODS:Diabetes-related datasets (GSE38642 and GSE44035) and stroke-related datasets (GSE16561 and GSE22255) were obtained from the Gene Expression omnibus (GEO) database. The key modules for stroke and diabetes were identified by weight gene co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) analyses were employed in the key module. Genes in stroke- and diabetes-related key modules were intersected to obtain common genes for T2DM-related stroke. In order to discover the key genes in T2DM-related stroke, the Cytoscape and protein-protein interaction (PPI) network were constructed. The key genes were functionally annotated in the Reactome database. RESULTS:By intersecting the diabetes- and stroke-related crucial modules, 24 common genes for T2DM-related stroke were identified. Metascape showed that neutrophil extracellular trap formation was primarily enriched. The hub gene was granulin precursor (GRN), which had the highest connectivity among the common genes. In addition, functional enrichment analysis indicated that GRN was involved in neutrophil degranulation, thus regulating neutrophil extracellular trap formation. CONCLUSIONS:This study firstly revealed that neutrophil extracellular trap formation may represent the common biological processes of diabetes and stroke, and GRN may be potential intervention targets for T2DM-related stroke.
10.1186/s12920-023-01752-z
Bioinformatics identification of potential biomarkers and therapeutic targets for ischemic stroke and vascular dementia.
Experimental gerontology
Ischemic stroke and vascular dementia, as common cerebrovascular diseases, with the former causing irreversible neurological damage and the latter causing cognitive and memory impairment, are closely related and have long received widespread attention. Currently, the potential causative genes of these two diseases have yet to be investigated, and effective early diagnostic tools for the diseases have not yet emerged. In this study, we screened new potential biomarkers and analyzed new therapeutic targets for both diseases from the perspective of immune infiltration. Two gene expression profiles on ischemic stroke and vascular dementia were obtained from the NCBI GEO database, and key genes were identified by LASSO regression and SVM-RFE algorithms, and key genes were analyzed by GO and KEGG enrichment. The CIBERSORT algorithm was applied to the gene expression profile species of the two diseases to quantify the 24 subpopulations of immune cells. Moreover, logistic regression modeling analysis was applied to illustrate the stability of the key genes in the diagnosis. Finally, the key genes were validated using RT-PCR assay. A total of 105 intersecting DEGs genes were obtained in the 2 sets of GEO datasets, and bioinformatics functional analysis of the intersecting DEGs genes showed that GO was mainly involved in the purine ribonucleoside triphosphate metabolic process,respiratory chain complex,DNA-binding transcription factor binding and active transmembrane transporter activity. KEGG is mainly involved in the Oxidative phosphorylation, cAMP signaling pathway. The LASSO regression algorithm and SVM-RFE algorithm finally obtained three genes, GAS2L1, ARHGEF40 and PFKFB3, and the logistic regression prediction model determined that the three genes, GAS2L1 (AUC: 0.882), ARHGEF40 (AUC: 0.867) and PFKFB3 (AUC: 0.869), had good diagnostic performance. Meanwhile, the two disease core genes and immune infiltration were closely related, GAS2L1 and PFKFB3 had the highest positive correlation with macrophage M1 (p < 0.001) and the highest negative correlation with mast cell activation (p = 0.0017); ARHGEF40 had the highest positive correlation with macrophage M1 and B cells naive (p < 0.001), the highest negative correlation with B cell memory highest correlation (p = 0.0047). RT-PCR results showed that the relative mRNA expression levels of GAS2L1, ARHGEF40, and PFKFB3 were significantly elevated in the populations of both disease groups (p < 0.05). Immune infiltration-based models can be used to predict the diagnosis of patients with ischemic stroke and vascular dementia and provide a new perspective on the early diagnosis and treatment of both diseases.
10.1016/j.exger.2024.112374
Identification and cross-validation of autophagy-related genes in cardioembolic stroke.
Frontiers in neurology
Objective:Cardioembolic stroke (CE stroke, also known as cardiogenic cerebral embolism, CCE) has the highest recurrence rate and fatality rate among all subtypes of ischemic stroke, the pathogenesis of which was unclear. Autophagy plays an essential role in the development of CE stroke. We aim to identify the potential autophagy-related molecular markers of CE stroke and uncover the potential therapeutic targets through bioinformatics analysis. Methods:The mRNA expression profile dataset GSE58294 was obtained from the GEO database. The potential autophagy-related differentially expressed (DE) genes of CE stroke were screened by R software. Protein-protein interactions (PPIs), correlation analysis, and gene ontology (GO) enrichment analysis were applied to the autophagy-related DE genes. GSE66724, GSE41177, and GSE22255 were introduced for the verification of the autophagy-related DE genes in CE stroke, and the differences in values were re-calculated by Student's -test. Results:A total of 41 autophagy-related DE genes (37 upregulated genes and four downregulated genes) were identified between 23 cardioembolic stroke patients (≤3 h, prior to treatment) and 23 healthy controls. The KEGG and GO enrichment analysis of autophagy-related DE genes indicated several enriched terms related to autophagy, apoptosis, and ER stress. The PPI results demonstrated the interactions between these autophagy-related genes. Moreover, several hub genes, especially for CE stroke, were identified and re-calculated by Student's -test. Conclusion:We identified 41 potential autophagy-related genes associated with CE stroke through bioinformatics analysis. SERPINA1, WDFY3, ERN1, RHEB, and BCL2L1 were identified as the most significant DE genes that may affect the development of CE stroke by regulating autophagy. CXCR4 was identified as a hub gene of all types of strokes. ARNT, MAPK1, ATG12, ATG16L2, ATG2B, and BECN1 were identified as particular hub genes for CE stroke. These results may provide insight into the role of autophagy in CE stroke and contribute to the discovery of potential therapeutic targets for CE stroke treatment.
10.3389/fneur.2023.1097623
An Artificial Intelligence Prediction Model of Insulin Sensitivity, Insulin Resistance, and Diabetes Using Genes Obtained through Differential Expression.
Genes
Insulin is a powerful pleiotropic hormone that affects processes such as cell growth, energy expenditure, and carbohydrate, lipid, and protein metabolism. The molecular mechanisms by which insulin regulates muscle metabolism and the underlying defects that cause insulin resistance have not been fully elucidated. This study aimed to perform a microarray data analysis to find differentially expressed genes. The analysis has been based on the data of a study deposited in Gene Expression Omnibus (GEO) with the identifier "GSE22309". The selected data contain samples from three types of patients after taking insulin treatment: patients with diabetes (DB), patients with insulin sensitivity (IS), and patients with insulin resistance (IR). Through an analysis of omics data, 20 genes were found to be differentially expressed (DEG) between the three possible comparisons obtained (DB vs. IS, DB vs. IR, and IS vs. IR); these data sets have been used to develop predictive models through machine learning (ML) techniques to classify patients with respect to the three categories mentioned previously. All the ML techniques present an accuracy superior to 80%, reaching almost 90% when unifying IR and DB categories.
10.3390/genes14122119
Identification of immune-associated genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning.
Frontiers in immunology
Background:In the pathogenesis of osteoarthritis (OA) and metabolic syndrome (MetS), the immune system plays a particularly important role. The purpose of this study was to find key diagnostic candidate genes in OA patients who also had metabolic syndrome. Methods:We searched the Gene Expression Omnibus (GEO) database for three OA and one MetS dataset. Limma, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms were used to identify and analyze the immune genes associated with OA and MetS. They were evaluated using nomograms and receiver operating characteristic (ROC) curves, and finally, immune cells dysregulated in OA were investigated using immune infiltration analysis. Results:After Limma analysis, the integrated OA dataset yielded 2263 DEGs, and the MetS dataset yielded the most relevant module containing 691 genes after WGCNA, with a total of 82 intersections between the two. The immune-related genes were mostly enriched in the enrichment analysis, and the immune infiltration analysis revealed an imbalance in multiple immune cells. Further machine learning screening yielded eight core genes that were evaluated by nomogram and diagnostic value and found to have a high diagnostic value (area under the curve from 0.82 to 0.96). Conclusion:Eight immune-related core genes were identified (, , , , , , , and ), and a nomogram for the diagnosis of OA and MetS was established. This research could lead to the identification of potential peripheral blood diagnostic candidate genes for MetS patients who also suffer from OA.
10.3389/fimmu.2023.1134412
Differently Expressed Genes (DEGs) Relevant to Type 2 Diabetes Mellitus Identification and Pathway Analysis via Integrated Bioinformatics Analysis.
Che Xuanqiang,Zhao Ran,Xu Hua,Liu Xue,Zhao Shumiao,Ma Hongwei
Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND The aim of this study was to evaluate the differently expressed genes (DEGs) relevant to type 2 diabetes mellitus (T2DM) and pathway by performing integrated bioinformatics analysis. MATERIAL AND METHODS The gene expression datasets GSE7014 and GSE29221 were downloaded in GEO database, and DEGs from type 2 diabetes mellitus and normal skeletal muscle tissues were identified. Biological function analysis of the DEGs was enriched by GO and KEEG pathway. A PPI network for the identified DEGs was built using the STRING database. RESULTS Thirty top DEGs were identified from 2 datasets: GSE7014 and GSE29221. Of the 30 top DEGs, 20 were up-regulated and 10 were down-regulated. The 20 up-regulated genes were enriched in regulation of mRNA, protein biding, and phospholipase D signaling pathway. The 10 down-regulated genes were enriched in telomere maintenance via semi-conservative replication, AGE-RAGE signaling pathway in diabetic complications, and insulin resistance pathway. In the PPI network of 20 up-regulated DEGs, there were 40 nodes and 84 edges, with an average node degree of 4.2. For the 10 down-regulated DEGs, we found a total of 30 nodes and 105 edges, with an average node degree of 7.0 and local clustering coefficient of 0.812. Among the 30 DEGs, 10 hub genes (CNOT6L, CNOT6, CNOT1, CNOT7, RQCD1, RFC2, PRIM1, RFC4, RFC5, and RFC1) were also identified through Cytoscape. CONCLUSIONS DEGs of T2DM may play an essential role in disease development and may be potential pathogeneses of T2DM.
10.12659/MSM.918407
Identification of Immune-Associated Genes in Diagnosing Aortic Valve Calcification With Metabolic Syndrome by Integrated Bioinformatics Analysis and Machine Learning.
Frontiers in immunology
Background:Immune system dysregulation plays a critical role in aortic valve calcification (AVC) and metabolic syndrome (MS) pathogenesis. The study aimed to identify pivotal diagnostic candidate genes for AVC patients with MS. Methods:We obtained three AVC and one MS dataset from the gene expression omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and module gene Limma and weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression and random forest) were used to identify candidate immune-associated hub genes for diagnosing AVC with MS. To assess the diagnostic value, the nomogram and receiver operating characteristic (ROC) curve were developed. Finally, immune cell infiltration was created to investigate immune cell dysregulation in AVC. Results:The merged AVC dataset included 587 DEGs, and 1,438 module genes were screened out in MS. MS DEGs were primarily enriched in immune regulation. The intersection of DEGs for AVC and module genes for MS was 50, which were mainly enriched in the immune system as well. Following the development of the PPI network, 26 node genes were filtered, and five candidate hub genes were chosen for nomogram building and diagnostic value evaluation after machine learning. The nomogram and all five candidate hub genes had high diagnostic values (area under the curve from 0.732 to 0.982). Various dysregulated immune cells were observed as well. Conclusion:Five immune-associated candidate hub genes (, , , , and ) were identified, and the nomogram was constructed for AVC with MS diagnosis. Our study could provide potential peripheral blood diagnostic candidate genes for AVC in MS patients.
10.3389/fimmu.2022.937886
Comprehensive Pan-Cancer Analysis of KIF18A as a Marker for Prognosis and Immunity.
Biomolecules
KIF18A belongs to the Kinesin family, which participates in the occurrence and progression of tumors. However, few pan-cancer analyses have been performed on KIF18A to date. We used multiple public databases such as TIMER, The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Human Protein Atlas (HPA) to explore KIF18A mRNA expression in 33 tumors. We performed immunohistochemistry on liver cancer and pancreatic cancer tissues and corresponding normal tissues to examine the expression of KIF18A protein. Univariate Cox regression and Kaplan-Meier survival analysis were applied to detect the effect of KIF18A on overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) of patients with these tumors. Subsequently, we explored KIF18A gene alterations in different tumor tissues using cBioPortal. The relationship between KIF18A and clinical characteristics, tumor microenvironment (TME), immune regulatory genes, immune checkpoints, tumor mutational burden (TMB), microsatellite instability (MSI), mismatch repairs (MMRs), DNA methylation, RNA methylation, and drug sensitivity was applied for further study using the R language. Gene Set Enrichment Analysis (GSEA) was utilized to explore the molecular mechanism of KIF18A. Bioinformatic analysis and immunohistochemical experiments confirmed that KIF18A was up-regulated in 27 tumors and was correlated with the T stage, N stage, pathological stage, histological grade, and Ki-67 index in many cancers. The overexpression of KIF18A had poor OS, DSS, and PFI in adrenocortical carcinoma (ACC), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), brain lower-grade glioma (LGG), liver cancer (LIHC), lung adenocarcinoma (LUAD), and pancreatic cancer (PAAD). Univariate and multivariate regression analysis confirmed KIF18A as an independent prognostic factor for LIHC and PAAD. The mutation frequency of KIF18A is the highest in endometrial cancer. KIF18A expression levels were positively associated with immunocyte infiltration, immune regulatory genes, immune checkpoints, TMB, MSI, MMRs, DNA methylation, RNA methylation, and drug sensitivity in certain cancers. In addition, we discovered that KIF18A participated in the cell cycle at the single-cell level and GSEA analysis for most cancers. These findings suggested that KIF18A could be regarded as a latent prognostic marker and a new target for cancer immunological therapy.
10.3390/biom13020326
Identification and development of a novel risk model based on cuproptosis-associated RNA methylation regulators for predicting prognosis and characterizing immune status in hepatocellular carcinoma.
Hepatology international
BACKGROUND:Cuproptosis, a novel cell death caused by excess copper, is quite obscure in hepatocellular carcinoma (HCC) and needs more investigation. METHODS:RNA-seq and clinical data of HCC patients TCGA database were analyzed to establish a predictive model through LASSO Cox regression analysis. External dataset ICGC was used for the verification. GSEA and CIBERSORT were applied to investigate the molecular mechanisms and immune microenvironment of HCC. Cuproptosis induced by elesclomol was confirmed via various in vitro experiments. The expression of prognostic genes was verified in HCC tissues using qRT-PCR analysis. RESULTS:Initially, 18 cuproptosis-associated RNA methylation regulators (CARMRs) were selected for prognostic analysis. A nine-gene signature was created by applying the LASSO Cox regression method. Survival and ROC assays were carried out to validate the model using TCGA and ICGC database. Moreover, there exhibited obvious differences in drug sensitivity in terms of common drugs. A higher tumor mutation burden was shown in the high-risk group. Additionally, significant discrepancies were found between the two groups in metabolic pathways and RNA processing via GSEA analysis. Meanwhile, CIBERSORT analysis indicated different infiltrating levels of various immune cells between the two groups. Elesclomol treatment caused a unique form of programmed cell death accompanied by loss of lipoylated mitochondrial proteins and Fe-S cluster protein. The results of qRT-PCR indicated that most prognostic genes were differentially expressed in the HCC tissues. CONCLUSION:Overall, our predictive signature displayed potential value in the prediction of overall survival of HCC patients and might provide valuable clues for personalized therapies.
10.1007/s12072-022-10460-2
The role of GnRH metabolite, GnRH-(1-5), in endometrial cancer.
Frontiers in endocrinology
From the time of its discovery and isolation in the mammalian hypothalamus, the decapeptide, gonadotropin-releasing hormone (GnRH), has also been found to be expressed in non-hypothalamic tissues and can elicit a diverse array of functions both in the brain and periphery. In cancer, past studies have targeted the gonadotropin-releasing hormone receptors (GnRHR) as a way to treat reproductive cancers due to its anti-tumorigenic effects. On the contrary, its metabolite, GnRH-(1-5), behaves divergently from its parental peptide through putative orphan G-protein coupled receptor (oGPCR), GPR101. In this review, we will focus on the potential roles of GnRH-(1-5) in the periphery with an emphasis on its effects on endometrial cancer progression.
10.3389/fendo.2023.1183278
Immunoglobulin Expression in Cancer Cells and Its Critical Roles in Tumorigenesis.
Cui Ming,Huang Jing,Zhang Shenghua,Liu Qiaofei,Liao Quan,Qiu Xiaoyan
Frontiers in immunology
Traditionally, immunoglobulin (Ig) was believed to be produced by only B-lineage cells. However, increasing evidence has revealed a high level of Ig expression in cancer cells, and this Ig is named cancer-derived Ig. Further studies have shown that cancer-derived Ig shares identical basic structures with B cell-derived Ig but exhibits several distinct characteristics, including restricted variable region sequences and aberrant glycosylation. In contrast to B cell-derived Ig, which functions as an antibody in the humoral immune response, cancer-derived Ig exerts profound protumorigenic effects multiple mechanisms, including promoting the malignant behaviors of cancer cells, mediating tumor immune escape, inducing inflammation, and activating the aggregation of platelets. Importantly, cancer-derived Ig shows promising potential for application as a diagnostic and therapeutic target in cancer patients. In this review, we summarize progress in the research area of cancer-derived Ig and discuss the perspectives of applying this novel target for the management of cancer patients.
10.3389/fimmu.2021.613530
Identification of lactylation related model to predict prognostic, tumor infiltrating immunocytes and response of immunotherapy in gastric cancer.
Frontiers in immunology
Background:The epigenetic regulatory chemical lactate is a product of glycolysis. It can regulate gene expression through histone lactylation, thereby promoting tumor proliferation, metastasis, and immunosuppression. Methods:In this study, a lactylation-related model for gastric cancer (GC) was constructed, and its relationships to prognosis, immune cell infiltration, and immunotherapy were investigated. By contrasting normal tissues and tumor tissues, four lactylation-related pathways that were substantially expressed in GC tissues were found in the GSEA database. Six lactylation-related genes were screened for bioinformatic analysis. The GC data sets from the TCGA and GEO databases were downloaded and integrated to perform cluster analysis, and the lactylation related model was constructed by secondary clustering. Results:The fingding demonstrated that the lactylation score has a strong correlation with the overall survival rate from GC and the progression of GC. Mechanistic experiments showed that abundant immune cell infiltration (macrophages showed the highest degree of infiltration) and increased genetic instability are traits of high lactylation scores. Immune checkpoint inhibitors (ICIs) demonstrated a reduced response rate in GC with high lactylation scores. At the same time, tumors with high lactylation scores had high Tumor Immune Dysfunction and Exclusion scores, which means that they had a higher risk of immune evasion and dysfunction. Discussion:These findings indicate that the lactylation score can be used to predict the malignant progression and immune evasion of GC. This model also can guide the treatment response to ICIs of GC. The constructed model of the lactate gene is also expected to become a potential therapeutic target for GC and diagnostic marker.
10.3389/fimmu.2023.1149989
Analysis and Experimental Validation of Rheumatoid Arthritis Innate Immunity Gene CYFIP2 and Pan-Cancer.
Frontiers in immunology
Rheumatoid arthritis (RA) is a chronic, heterogeneous autoimmune disease. Its high disability rate has a serious impact on society and individuals, but there is still a lack of effective and reliable diagnostic markers and therapeutic targets for RA. In this study, we integrated RA patient information from three GEO databases for differential gene expression analysis. Additionally, we also obtained pan-cancer-related genes from the TCGA and GTEx databases. For RA-related differential genes, we performed functional enrichment analysis and constructed a weighted gene co-expression network (WGCNA). Then, we obtained 490 key genes by intersecting the significant module genes selected by WGCNA and the differential genes. After using the RanddomForest, SVM-REF, and LASSO three algorithms to analyze these key genes and take the intersection, based on the four core genes (BTN3A2, CYFIP2, ST8SIA1, and TYMS) that we found, we constructed an RA diagnosis. The nomogram model showed good reliability and validity after evaluation, and the ROC curves of the four genes showed that these four genes played an important role in the pathogenesis of RA. After further gene correlation analysis, immune infiltration analysis, and mouse gene expression validation, we finally selected CYFIP2 as the cut-in gene for pan-cancer analysis. The results of the pan-cancer analysis showed that CYFIP2 was closely related to the prognosis of patients with various tumors, the degree of immune cell infiltration, as well as TMB, MSI, and other indicators, suggesting that this gene may be a potential intervention target for human diseases including RA and tumors.
10.3389/fimmu.2022.954848
Identification and Analysis of Gene Biomarkers for Ovarian Cancer.
Genetic testing and molecular biomarkers
To identify potential diagnostic markers for ovarian cancer (OC) and explore the contribution of immune cells infiltration to the pathogenesis of OC. As the study cohort, two gene expression datasets of human OC (GSE27651 and GSE26712, taken as the metadata) taken from the Gene Expression Omnibus (GEO) database were combined, comprising 228 OC and 16 control samples. Analysis was performed to identify the differentially expressed genes between the OC and control samples, while support vector machine analysis using the recursive feature elimination algorithm and least absolute shrinkage and selection operator regression were performed to identify candidate biomarkers that could discriminate OC. In addition, immunohistochemistry staining was performed to verify the diagnostic value and protein expression levels of the candidate biomarkers. The GSE146553 dataset (OC = 40, control = 3) was used to further validate the diagnostic values of those biomarkers. Further, the proportions of various immune cells infiltration in the OC and control samples were evaluated using the CIBERSORT algorithm. CLEC4M, PFKP, and SCRIB were identified as potential diagnostic markers for OC in both the metadata (area under the receiver operating characteristic curve [AUC] = 0.996, AUC = 1.000, AUC = 1.000) and GSE146553 dataset (AUC = 0.983, AUC = 0.975, AUC = 0.892). Regarding immune cell infiltration, there was an increase in the infiltration of follicular helper dendritic cells, and a decrease in the infiltration of M2 macrophages and neutrophils, as well as activated natural killer (NK) cells and T cells in OC. CLEC4M showed a significantly positive correlation with neutrophils ( = 0.57, < 0.001) and resting NK cells ( = 0.42, = 0.0047), but a negative correlation with activated dendritic cells ( = -0.33, = 0.032). PFKP displayed a significantly positive correlation with activated NK cells ( = 0.36, = 0.016) and follicular helper T cells ( = 0.32, = 0.035), but a negative correlation with the naive B cells ( = -0.3, = 0.049) and resting NK cells ( = -0.41, = 0.007). SCRIB demonstrated a significantly positive correlation with plasma cells ( = 0.39, = 0.01), memory B cells ( = 0.34, = 0.025), and follicular helper T cells ( = 0.31, = 0.04), but a negative correlation with neutrophils ( = -0.46, = 0.002) and naive B cells ( = -0.48, = 0.0012). CLEC4M, PFKP, and SCRIB were identified and verified as potential diagnostic biomarkers for OC. This work and identification of the three biomarkers may provide guidance for future studies into the mechanism and treatment of OC.
10.1089/gtmb.2023.0222
Inflammation as a risk factor for stroke in atrial fibrillation: data from a microarray data analysis.
Li Yingyuan,Tan Wulin,Ye Fang,Wen Shihong,Hu Rong,Cai Xiaoying,Wang Kebing,Wang Zhongxing
The Journal of international medical research
OBJECTIVE:Stroke is a severe complication of atrial fibrillation (AF). We aimed to discover key genes and microRNAs related to stroke risk in patients with AF using bioinformatics analysis. METHODS:GSE66724 microarray data, including peripheral blood samples from eight patients with AF and stroke and eight patients with AF without stroke, were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AF patients with and without stroke were identified using the GEO2R online tool. Functional enrichment analysis was performed using the DAVID database. A protein-protein interaction (PPI) network was obtained using the STRING database. MicroRNAs (miRs) targeting these DEGs were obtained from the miRNet database. A miR-DEG network was constructed using Cytoscape software. RESULTS:We identified 165 DEGs (141 upregulated and 24 downregulated). Enrichment analysis showed enrichment of certain inflammatory processes. The miR-DEG network revealed key genes, including , , , and , and microRNAs, including miR-1, miR-1-3p, miR-21, miR-21-5p, miR-192, miR-192-5p, miR-155, and miR-155-5p. CONCLUSION:Dysregulation of certain genes and microRNAs involved in inflammation may be associated with a higher risk of stroke in patients with AF. Evaluating these biomarkers could improve prediction, prevention, and treatment of stroke in patients with AF.
10.1177/0300060520921671
Transcriptomic analysis reveals the potential crosstalk genes and immune relationship between Crohn's disease and atrial fibrillation.
Journal of thoracic disease
Background:At present, there is a paucity of research on the link between Crohn's disease (CD) and atrial fibrillation (AF). Nevertheless, both ailments are thought to entail inflammatory and autoimmune processes, and emerging evidence indicates that individuals with CD may face an elevated risk of AF. To shed light on this issue, our study seeks to explore the possibility of shared genes, pathways, and immune cells between these two conditions. Methods:We retrieved the gene expression profiles of both CD and AF from the Gene Expression Omnibus (GEO) database and subjected them to analysis. Afterward, we utilized the weighted gene co-expression network analysis (WGCNA) to identify shared genes, which were then subjected to further Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Furthermore, we employed a rigorous analytical approach by screening hub genes through both least absolute shrinkage and selection operator (LASSO) regression and support vector machine (SVM), and subsequently constructing a receiver operating characteristic (ROC) curve based on the screening outcomes. Finally, we utilized single-sample gene set enrichment analysis (ssGSEA) to comprehensively evaluate the levels of infiltration of 28 immune cells within the expression profile and their potential association with the shared hub genes. Results:Using the WGCNA method, we identified 30 genes that appear to be involved in the pathological progression of both AF and CD. Through GO enrichment analysis on the key gene modules derived from WGCNA, we observed a significant enrichment of pathways related to major histocompatibility complex (MHC) and antigen processing. By leveraging the intersection of LASSO and SVM algorithms, we were able to pinpoint two overlapping genes, namely and . Additionally, we evaluated the infiltration of immune cells and observed the upregulation of CD4 and CD8 T cells, as well as dendritic cells in patients with AF and CD. Conclusions:By employing bioinformatics tools, we conducted an investigation with the objective of elucidating the genetic foundations that connect AF and CD. This study culminated in the identification of and as the most substantial genes implicated in the development of both disorders. Our findings suggest that the immune responses mediated by CD4 and CD8 T cells, along with dendritic cells, may hold a crucial role in the intricate interplay between AF and CD.
10.21037/jtd-23-1078
Uncovering potential novel biomarkers and immune infiltration characteristics in persistent atrial fibrillation using integrated bioinformatics analysis.
Mathematical biosciences and engineering : MBE
Atrial fibrillation (AF) is the most common cardiac arrhythmia. This study aimed to identify potential novel biomarkers for persistent AF (pAF) using integrated analyses and explore the immune cell infiltration in this pathological process. Three pAF datasets (GSE31821, GSE41177, and GSE79768) from the Gene Expression Omnibus (GEO) database were integrated with the elimination of batch effects. 264 differentially expressed genes (DEGs) were identified using Linear models for microarray data (LIMMA), 12 modules were screened out by weighted gene co-expression network analysis (WGCNA) in pAF compared with normal controls. Subsequently, common genes (CGs) were identified as the intersection of DEGs and genes in the most significant module. Functional enrichment analysis showed that CGs were mainly enriched in the "Calcineurin-NFAT (nuclear factor of activated T-cells)" signaling pathway, particularly regulator of calcineurin 1 (), and protein phosphatase 3 regulatory subunit B, alpha (). Ulteriorly, the microRNA-transcription factor-mRNA network revealed that microRNA-34a-5p could target both and in the pAF pathogenesis. Finally, immune infiltration analysis by CIBERSORT, a versatile computational method, displayed a higher level of monocytes, dendritic cells and neutrophils, as well as a lower level of CD8+ T cells and T cells regulatory (Tregs) in pAF compared with the control group. In conclusion, our present study revealed several novel pAF-associated genes, miRNAs, and pathways, including microRNA-34a-5p, which might target and to regulate pAF through the calcineurin-NFAT signaling pathway. In addition, there was a difference in immune infiltration between patients with pAF and normal groups and immune cells might interact with specific genes in pAF.
10.3934/mbe.2021238
Bioinformatics and functional experiments reveal that inhibits atrial fibrillation via the PPAR signaling pathway.
Journal of thoracic disease
Background:Atrial fibrillation (AF) is a prevalent cardiac arrhythmia that requires improved clinical markers to increase diagnostic accuracy and provide insight into its pathogenesis. Although some biomarkers are available, new ones need to be discovered to better capture the complex physiology of AF. However, their limitations are still not fully addressed. Bioinformatics and functional studies can help find new clinical markers and improve the understanding of AF, meeting the need for early diagnosis and individualized treatment. Methods:To identify AF-related differentially expressed genes (DEGs), We applied the messenger RNA (mRNA) expression profile retrieved in Series Matrix File format from the GSE143924 microarray dataset obtained from the Gene Expression Omnibus (GEO) database, and then used weighted gene co-expression network analysis (WGCNA) to identify the overlapping genes. These genes were analyzed by enrichment analysis, expression analysis and others to obtain the hub gene. Additionally, the potential signaling pathway of hub gene in AF was explored and verified by functional experiments, like quantitative real-time polymerase chain reaction (qRT-PCR), cell counting kit-8 (CCK-8), flow cytometry, and Western blotting (WB) assay. Results:From the GSE143924 data (410 DEGs) and tan module (57 genes), 10 overlapping genes were identified. A central down-regulated gene in AF, , was identified through bioinformatics analysis. based on these results, it was hypothesized that the PPAR signaling pathway is related to the mechanism of action of in AF. Moreover, over- markedly reduced the growth speed of angiotensin II (Ang II)-induced human cardiac fibroblasts (HCFs) and increased apoptosis. Conversely, knockdown of promoted HCFs cell proliferation number. Additionally, over-expression increased the protein expression level of PPARα, PPARγ, CPT-1, and SIRT3 in Ang II-induced HCFs. Conclusions:While meeting the need for new biomarkers in the diagnosis and prognosis of AF, this study reveals the inherent limitations of current biomarkers. We identified as a key player as an inhibitory gene in AF, highlighting its role in suppressing AF progression through the PPAR signaling pathway. may not only serve as a diagnostic indicator, but also as a promising therapeutic target for patients with AF, which is expected to be applied in clinical practice and open up new avenues for individualized interventions.
10.21037/jtd-23-1235
The Four Key Genes Participated in and Maintained Atrial Fibrillation Process Reprogramming Lipid Metabolism in AF Patients.
Frontiers in genetics
Atrial fibrillation (AF) is always in high incidence in the population, which can lead to serious complications. The structural and electrical remodeling of atrial muscle induced by inflammatory reaction or oxidative stress was considered as the major mechanism of AF. The treatment effect is not ideal based on current mechanisms. Recent studies demonstrated that lipid metabolism disorder of atrial muscle played an important role in the occurrence of AF. What key genes are involved is unclear. The purpose of the present study was to explore the lipid metabolism mechanism of AF. With the GEO database and the genomics of AF patients, metabolic related pathways and the key genes were analyzed. At the same time, the rat model of cecal ligation and puncture (CLP) was used to confirm the results. GSE 31821 and GSE 41177 were used as data sources, and the merged differentially expressed genes (DEGs) analysis showed that a total of 272 DEGs were found. GO annotation, KEGG, and gene set enrichment analysis (GSEA) showed that the fatty acid metabolism and the lipid biosynthetic process were involved in AF. Cholesterol biosynthesis, arachidonic acid metabolism, and the lipid droplet pathway were obviously increased in AF. Further analysis showed that four key genes, including ITGB1, HSP90AA1, CCND1, and HSPA8 participated in pathogenesis of AF regulating lipid biosynthesis. In CLP rats, metabolic profiling in the heart showed that the pyrimidine metabolism, the biosynthesis of unsaturated fatty acid metabolism, arginine and proline metabolism, and the fatty acid biosynthesis were involved. The four key genes were confirmed increased in the heart of CLP rats ( < 0.05 or 0.01). The results suggest that the lipid metabolism disorder participates in the occurrence of AF. ITGB1, HSP90AA1, CCND1, and HSPA8 are the key genes involved in the regulation of lipid biosynthesis.
10.3389/fgene.2022.821754
C1QC, VSIG4, and CFD as Potential Peripheral Blood Biomarkers in Atrial Fibrillation-Related Cardioembolic Stroke.
Oxidative medicine and cellular longevity
Atrial fibrillation (AF) is a major risk factor for ischemic stroke. We aimed to identify novel potential biomarkers with diagnostic value in patients with atrial fibrillation-related cardioembolic stroke (AF-CE).Publicly available gene expression profiles related to AF, cardioembolic stroke (CE), and large artery atherosclerosis (LAA) were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified and then functionally annotated. The support vector machine recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) regression analysis were conducted to identify potential diagnostic AF-CE biomarkers. Furthermore, the results were validated by using external data sets, and discriminability was measured by the area under the ROC curve (AUC). In order to verify the predictive results, the blood samples of 13 healthy controls, 20 patients with CE, and 20 patients with LAA stroke were acquired for RT-qPCR, and the correlation between biomarkers and clinical features was further explored. Lastly, a nomogram and the companion website were developed to predict the CE-risk rate. Three feature genes (C1QC, VSIG4, and CFD) were selected and validated in the training and the external datasets. The qRT-PCR evaluation showed that the levels of blood biomarkers (C1QC, VSIG4, and CFD) in patients with AF-CE can be used to differentiate patients with AF-CE from normal controls ( < 0.05) and can effectively discriminate AF-CE from LAA stroke ( < 0.05). Immune cell infiltration analysis revealed that three feature genes were correlated with immune system such as neutrophils. Clinical impact curve, calibration curves, ROC, and DCAs of the nomogram indicate that the nomogram had good performance. Our findings showed that C1QC, VSIG4, and CFD can potentially serve as diagnostic blood biomarkers of AF-CE; novel nomogram and the companion website can help clinicians to identify high-risk individuals, thus helping to guide treatment decisions for stroke patients.
10.1155/2023/5199810
Mining of Potential Biomarkers and Pathway in Valvular Atrial Fibrillation (VAF) via Systematic Screening of Gene Coexpression Network.
Computational and mathematical methods in medicine
Purpose:We apply the bioinformatics method to excavate the potential genes and therapeutic targets associated with valvular atrial fibrillation (VAF). Methods:The downloaded gene expression files from the gene expression omnibus (GEO) included patients with primary severe mitral regurgitation complicated with sinus or atrial fibrillation rhythm. Subsequently, the differential gene expression in left and right atrium was analyzed by R software. Additionally, weighted correlation network analysis (WGCNA), principal component analysis (PCA), and linear model for microarray data (LIMMA) algorithm were used to determine hub genes. Then, Metascape database, DAVID database, and STRING database were used to annotate and visualize the gene ontology (GO) analysis, KEGG pathway enrichment analysis, and PPI network analysis of differentially expressed genes (DEGs). Finally, the TFs and miRNAs were predicted by using online tools, such as PASTAA and miRDB. Results:20,484 differentially expressed genes related to atrial fibrillation were obtained through the analysis of left and right atrial tissue samples of GSE115574 gene chip, and 1,009 were with statistical significance, including 45 upregulated genes and 964 downregulated genes. And the hub genes implicated in AF of NPC2, ODC1, SNAP29, LAPTM5, ST8SIA5, and FCGR3B were screened. Finally, the main regulators of targeted candidate biomarkers and microRNAs, EIF5A2, HIF1A, ZIC2, ELF1, and STAT2, were found in this study. Conclusion:These hub genes, NPC2, ODC1, SNAP29, LAPTM5, ST8SIA5, and FCGR3B, are important for the development of VAF, and their enrichment pathways and TFs elucidate the involved molecular mechanisms and assist in the validation of drug targets.
10.1155/2022/3645402
Identification of Genes and Key Pathways Associated with the Pathophysiology of Lung Cancer and Atrial Fibrillation.
Alternative therapies in health and medicine
Background:Lung cancer is a malignant tumor originating from respiratory epithelial cells in the bronchi, bronchioles, and alveoli, often associated with atrial fibrillation; However, there is a lack of in-depth understanding of its genetic basis and molecular mechanisms. Our goal is to study the genes and signaling networks associated with cancer and atrial fibrillation. Materials and methods:We obtained microarray datasets for lung tumors from the Gene Expression Omnibus (GEO) database and AF for this investigation: GSE30219, GSE79768, and screened the candidate specimens in both microarrays for differential genes at P < .05 using GEO2R. The outcomes were also examined using the Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Gene and Gene Combinations (KEGG) pathway analysis methods. Using STRING and Cytoscape, protein interaction networks (PPI) were analyzed and visualized. The Molecular Complex Detection (MOCDE) plugin is responsible for filtering important compounds. Candidate genes are then screened by the cytoHubba plugin according to MCC criteria. After taking the intersection of the Hub genes by the Wayne diagram, the ROC curves were plotted separately by combining the data from one lung cancer dataset GSE19804, two AF datasets GSE41177/GSE14975 in the GEO database. Results:An aggregate of 49 co-expressed differentially expressed genes (co-DEGs) were discovered in lung cancer/AF and healthy controls. Most co-DEGs were found in neutrophil activation, where they were linked to immunological response and interactions between cytokines and cytokine receptors, according to GO and KEGG pathway analyses. Furthermore, due of their significant connectedness in both the lung carcinoma and AF datasets, we chose six key genes. They are MNDA, HP, LYZ, S100A9, S100A8, and S100A12, among others. Conclusions:The findings of this research indicate that the onset of lung cancer and AF depends on a small number of distinctive genes. We investigated the functional enrichment, differential gene expression, and PPI of DEGs in lung cancer and AF, and the results offer fresh perspectives on the discovery of prospective biomarkers and priceless therapeutic precursors in these two diseases.
Bioinformatics analysis reveals the potential common genes and immune characteristics between atrial fibrillation and periodontitis.
Journal of periodontal research
BACKGROUND AND OBJECTIVE:Atrial fibrillation (AF) and periodontitis, both classified under chronic inflammatory diseases, share common etiologies, including genetic factors and immune pathways. However, the exact mechanisms are still poorly understood. This study aimed to explore the potential common genes and immune characteristics between AF and periodontitis. METHODS:Gene expression datasets for AF and periodontitis were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis was used to identify common genes in the training set. Functional analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, were conducted to elucidate the underlying mechanisms. Hub genes were further screened based on expression levels, receiver operating characteristic (ROC) curves, and least absolute shrinkage and selection operator (LASSO) regression. Then, based on the expression levels and ROC values of the hub genes in the validation set, the target genes were identified. Finally, immune cell infiltration analysis was performed on the AF and periodontitis datasets in the training set using the "CIBERSORT" R package. The relationships between target genes, infiltrating immune cells, and inflammatory factors were also investigated. In addition, AF susceptibility, atrial fibrosis, inflammatory infiltration, and RGS1 protein expression in rat models of periodontitis were assessed through in vivo electrophysiology experiments, Masson's trichrome staining, hematoxylin-eosin staining, immunohistochemistry, and western blotting, respectively. RESULTS:A total of 21 common genes were identified between AF and periodontitis among the differentially expressed genes. After evaluating gene expression levels, ROC curves, and LASSO analysis, four significant genes between AF and periodontitis were identified, namely regulator of G-protein signaling 1 (RGS1), annexin A6 (ANXA6), solute carrier family 27 member 6 (SLC27A6), and ficolin 1 (FCN1). Further validation confirmed that RGS1 was the optimal shared target gene for AF and periodontitis. Immune cell infiltration analysis revealed that neutrophils and T cells play an important role in the pathogenesis of both diseases. RGS1 showed a significant positive correlation with activated memory CD4 T cells and gamma-delta T cells and a negative correlation with CD8 T cells and regulatory T cells in both training sets. Moreover, RGS1 was positively correlated with classical pro-inflammatory cytokines IL1β and IL6. In periodontitis rat models, AF susceptibility, atrial fibrosis, and inflammatory infiltration were significantly increased, and RGS1 expression in the atrial tissue was upregulated. CONCLUSION:A common gene between AF and periodontitis, RGS1 appears central in linking the two conditions. Immune and inflammatory responses may underlie the interaction between AF and periodontitis.
10.1111/jre.13192
and as Atrial Fibrillation Susceptibility Genes in the Chinese Population via Bioinformatics and Genome-Wide Association Analysis.
Biomedicines
BACKGROUND:Atrial fibrillation (AF) is the most common cardiac arrhythmia, with uncovered genetic etiology and pathogenesis. We aimed to screen out AF susceptibility genes with potential pathogenesis significance in the Chinese population. METHODS:Differentially expressed genes (DEGs) were screened by the Limma package in three GEO data sets of atrial tissue. AF-related genes were identified by combination of DEGs and public GWAS susceptibility genes. Potential drug target genes were selected using the DrugBank, STITCH and TCMSP databases. Pathway enrichment analyses of AF-related genes were performed using the databases GO and KEGG databases. The pathway gene network was visualized by Cytoscape software to identify gene-gene interactions and hub genes. GWAS analysis of 110 cases of AF and 1201 controls was carried out through a genome-wide efficient mixed model in the Fangshan population to verify the results of bioinformatic analysis. RESULTS:A total of 3173 DEGs were identified, 57 of which were found to be significantly associated with of AF in public GWAS results. A total of 75 AF-related genes were found to be potential therapeutic targets. Pathway enrichment analysis selected 79 significant pathways and classified them into 7 major pathway networks. A total of 35 hub genes were selected from the pathway networks. GWAS analysis identified 126 AF-associated loci. and were found to be overlapping genes between bioinformatic analysis and GWAS analysis. CONCLUSIONS:We screened out several pivotal genes and pathways involved in AF pathogenesis. Among them, and were significantly associated with the risk of AF in the Chinese population. Our study provided new insights into the mechanisms of action of AF.
10.3390/biomedicines11030908
Mechanisms of Quercetin against atrial fibrillation explored by network pharmacology combined with molecular docking and experimental validation.
Scientific reports
Atrial fibrillation (AF) is a common atrial arrhythmia for which there is no specific therapeutic drug. Quercetin (Que) has been used to treat cardiovascular diseases such as arrhythmias. In this study, we explored the mechanism of action of Que in AF using network pharmacology and molecular docking. The chemical structure of Que was obtained from Pubchem. TCMSP, Swiss Target Prediction, Drugbank, STITCH, Pharmmapper, CTD, GeneCards, DISGENET and TTD were used to obtain drug component targets and AF-related genes, and extract AF and normal tissue by GEO database differentially expressed genes by GEO database. The top targets were IL6, VEGFA, JUN, MMP9 and EGFR, and Que for AF treatment might involve the role of AGE-RAGE signaling pathway in diabetic complications, MAPK signaling pathway and IL-17 signaling pathway. Molecular docking showed that Que binds strongly to key targets and is differentially expressed in AF. In vivo results showed that Que significantly reduced the duration of AF fibrillation and improved atrial remodeling, reduced p-MAPK protein expression, and inhibited the progression of AF. Combining network pharmacology and molecular docking approaches with in vivo studies advance our understanding of the intensive mechanisms of Quercetin, and provide the targeted basis for clinical Atrial fibrillation treatment.
10.1038/s41598-022-13911-w
Identification and validation of aging-related genes in atrial fibrillation.
PloS one
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in the clinic. Aging plays an essential role in the occurrence and development of AF. Herein, we aimed to identify the aging-related genes associated with AF using bioinformatics analysis. Transcriptome profiles of AF were obtained from the GEO database. Differential expression analysis was performed to identify AF-specific aging-related genes. GO and KEGG enrichment analyses were performed. Subsequently, the LASSO, SVM-RFE, and MCC algorithms were applied to screen aging-related genes. The mRNA expression of the screened genes was validated in the left atrial samples of aged rapid atrial pacing-induced AF canine models and their counterparts. The ROC curves of them were drawn to evaluate their diagnostic potential. Moreover, CIBERSORT was used to estimate immune infiltration. A correlation analysis between screened aging-related genes and infiltrating immune cells was performed. A total of 24 aging-related genes were identified, which were found to be mainly involved in the FoxO signaling pathway, PI3K-Akt signaling pathway, longevity regulating pathway, and peroxisome according to functional enrichment analysis. LASSO, SVM-RFE, and MCC algorithms identified three genes (HSPA9, SOD2, TXN). Furthermore, the expression levels of HSPA9 and SOD2 were validated in aged rapid atrial pacing-induced AF canine models. HSPA9 and SOD2 could be potential diagnostic biomarkers for AF, as evidenced by the ROC curves. Immune infiltration and correlation analysis revealed that HSPA9 and SOD2 were related to immune cell infiltrates. Collectively, these findings provide novel insights into the potential aging-related genes associated with AF. HSPA9 and SOD2 may play a significant role in the occurrence and development of AF.
10.1371/journal.pone.0294282
Bioinformatic gene analysis for potential biomarkers and therapeutic targets of atrial fibrillation-related stroke.
Zou Rongjun,Zhang Dingwen,Lv Lei,Shi Wanting,Song Zijiao,Yi Bin,Lai Bingjia,Chen Qian,Yang Songran,Hua Ping
Journal of translational medicine
BACKGROUND:Atrial fibrillation (AF) is one of the most prevalent sustained arrhythmias, however, epidemiological data may understate its actual prevalence. Meanwhile, AF is considered to be a major cause of ischemic strokes due to irregular heart-rhythm, coexisting chronic vascular inflammation, and renal insufficiency, and blood stasis. We studied co-expressed genes to understand relationships between atrial fibrillation (AF) and stroke and reveal potential biomarkers and therapeutic targets of AF-related stroke. METHODS:AF-and stroke-related differentially expressed genes (DEGs) were identified via bioinformatic analysis Gene Expression Omnibus (GEO) datasets GSE79768 and GSE58294, respectively. Subsequently, extensive target prediction and network analyses methods were used to assess protein-protein interaction (PPI) networks, Gene Ontology (GO) terms and pathway enrichment for DEGs, and co-expressed DEGs coupled with corresponding predicted miRNAs involved in AF and stroke were assessed as well. RESULTS:We identified 489, 265, 518, and 592 DEGs in left atrial specimens and cardioembolic stroke blood samples at < 3, 5, and 24 h, respectively. LRRK2, CALM1, CXCR4, TLR4, CTNNB1, and CXCR2 may be implicated in AF and the hub-genes of CD19, FGF9, SOX9, GNGT1, and NOG may be associated with stroke. Finally, co-expressed DEGs of ZNF566, PDZK1IP1, ZFHX3, and PITX2 coupled with corresponding predicted miRNAs, especially miR-27a-3p, miR-27b-3p, and miR-494-3p may be significantly associated with AF-related stroke. CONCLUSION:AF and stroke are related and ZNF566, PDZK1IP1, ZFHX3, and PITX2 genes are significantly associated with novel biomarkers involved in AF-related stroke.
10.1186/s12967-019-1790-x
The influence of pyroptosis-related genes on the development of chronic obstructive pulmonary disease.
BMC pulmonary medicine
Increasing evidences have demonstrated that pyroptosis exerts key roles in the occurrence, development of chronic obstructive pulmonary disease. However, the mechanisms of pyroptosis in COPD remain largely unknown. In our research, Statistics were performed using R software and related packages in this study. Series matrix files of small airway epithelium samples were downloaded from the GEO database. Differential expression analysis with FDR < 0.05 was performed to identify COPD-associated pyroptosis-related genes. 8 up-regulated genes (CASP4, CASP5, CHMP7, GZMB, IL1B, AIM2, CASP6, GSDMC) and 1 down-regulated genes (PLCG1) was identified as COPD-associated pyroptosis-related genes. Twenty-six COPD key genes was identified by WGCNA analysis. PPI analysis and gene correlation analysis showed their relationship clearly. KEGG and GO analysis have revealed the main pyroptosis-related mechanism of COPD. The expression of 9 COPD-associated pyroptosis-related genes in different grades was also depicted. The immune environment of COPD was also explored. Furthermore, the relationship of pyroptosis-related genes and the expression of immune cells was also be shown in the end. In the end, we concluded that pyroptosis influences the development of COPD. This study may provide new insight into the novel therapeutic targets for COPD clinical treatment.
10.1186/s12890-023-02408-5
Combining bioinformatics and machine learning to identify common mechanisms and biomarkers of chronic obstructive pulmonary disease and atrial fibrillation.
Frontiers in cardiovascular medicine
Background:Patients with chronic obstructive pulmonary disease (COPD) often present with atrial fibrillation (AF), but the common pathophysiological mechanisms between the two are unclear. This study aimed to investigate the common biological mechanisms of COPD and AF and to search for important biomarkers through bioinformatic analysis of public RNA sequencing databases. Methods:Four datasets of COPD and AF were downloaded from the Gene Expression Omnibus (GEO) database. The overlapping genes common to both diseases were screened by WGCNA analysis, followed by protein-protein interaction network construction and functional enrichment analysis to elucidate the common mechanisms of COPD and AF. Machine learning algorithms were also used to identify key biomarkers. Co-expression analysis, "transcription factor (TF)-mRNA-microRNA (miRNA)" regulatory networks and drug prediction were performed for key biomarkers. Finally, immune cell infiltration analysis was performed to evaluate further the immune cell changes in the COPD dataset and the correlation between key biomarkers and immune cells. Results:A total of 133 overlapping genes for COPD and AF were obtained, and the enrichment was mainly focused on pathways associated with the inflammatory immune response. A key biomarker, cyclin dependent kinase 8 (), was identified through screening by machine learning algorithms and validated in the validation dataset. Twenty potential drugs capable of targeting were obtained. Immune cell infiltration analysis revealed the presence of multiple immune cell dysregulation in COPD. Correlation analysis showed that expression was significantly associated with CD8+ T cells, resting dendritic cell, macrophage M2, and monocytes. Conclusions:This study highlights the role of the inflammatory immune response in COPD combined with AF. The prominent link between and the inflammatory immune response and its characteristic of not affecting the basal expression level of nuclear factor kappa B ( make it a possible promising therapeutic target for COPD combined with AF.
10.3389/fcvm.2023.1121102
Revealing EXPH5 as a potential diagnostic gene biomarker of the late stage of COPD based on machine learning analysis.
Computers in biology and medicine
Chronic obstructive pulmonary disease is a kind of chronic lung disease characterized by persistent air flow obstruction, which was the third leading cause of death in China. The incidence of COPD is steadily and increasing and has been a globally sever disease. Accordingly, it is urgently needed to explore how to diagnose and treat COPD timely. This study aims to find key genes to diagnose COPD as soon as possible to avoid COPD processing and analyze immune cell infiltration between COPD early stage and late stage. Two GEO datasets were merged as the merge data for analyses. 157 DEGs were used for GSEA analysis to find the pathway between COPD early stage and late stage. Above all, gene EXPH5 stood out from the screen as the most likely candidate diagnosis biomarker of COPD indicating the late-stage by least LASSO and SVM-RFE. ROC curves of EXPH5 were applied to represent the discriminatory ability through the area under the curve which is the gold standard to evaluate the accuracy of diagnosis and survival rate. The CIBERSORT algorithm was used to assess the distribution of tissue-infiltrating immune cells between two COPD stages. The diagnosis biomarker, gene EXPH5 had a positive correlation with NK cells resting; mast cell resting, eosinophils, and negative correlation with T cell gamma delta, macrophages M1, which underscore the role of gene and immune cell infiltration. To make results more reliable, we further analyzed the gene EXPH5 expression in single-cell transcriptome data and showed again that EXPH5 genes significantly downregulated in the late stage of COPD especially in the main lung cell types AT1 and AT2. In a word, our study identified genes EXPH5 as a marker gene, which adds to the knowledge for clinical diagnosis and pharmaceutical design of COPD.
10.1016/j.compbiomed.2023.106621
Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients.
Mahmud S M Hasan,Al-Mustanjid Md,Akter Farzana,Rahman Md Shazzadur,Ahmed Kawsar,Rahman Md Habibur,Chen Wenyu,Moni Mohammad Ali
Briefings in bioinformatics
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein-protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors-genes interaction, protein-drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.
10.1093/bib/bbab115
Identification and Validation of Autophagy-Related Genes in Chronic Obstructive Pulmonary Disease.
Sun Shulei,Shen Yuehao,Wang Jie,Li Jinna,Cao Jie,Zhang Jing
International journal of chronic obstructive pulmonary disease
Purpose:Autophagy plays essential roles in the development of COPD. We aim to identify and validate the potential autophagy-related genes of COPD through bioinformatics analysis and experiment validation. Methods:The mRNA expression profile dataset GSE38974 was obtained from GEO database. The potential differentially expressed autophagy-related genes of COPD were screened by R software. Then, protein-protein interactions (PPI), correlation analysis, gene-ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were applied for the differentially expressed autophagy-related genes. Finally, RNA expression of top five differentially expressed autophagy-related genes was validated in blood samples from COPD patients and healthy controls by qRT-PCR. Results:A total of 40 differentially expressed autophagy-related genes (14 up-regulated genes and 26 down-regulated genes) were identified between 23 COPD patients and 9 healthy controls. The PPI results demonstrated that these autophagy-related genes interacted with each other. The GO and KEGG enrichment analysis of differentially expressed autophagy-related genes indicated several enriched terms related to autophagy and mitophagy. The results of qRT-PCR showed that the expression levels of and in COPD patients and healthy controls were consistent with the bioinformatics analysis results from mRNA microarray. Conclusion:We identified 40 potential autophagy-related genes of COPD through bioinformatics analysis. and may affect the development of COPD by regulating autophagy. These results may expand our understanding of COPD and might be useful in the treatment of COPD.
10.2147/COPD.S288428
Machine-Learning Algorithm-Based Prediction of Diagnostic Gene Biomarkers Related to Immune Infiltration in Patients With Chronic Obstructive Pulmonary Disease.
Frontiers in immunology
Objective:This study aims to identify clinically relevant diagnostic biomarkers in chronic obstructive pulmonary disease (COPD) while exploring how immune cell infiltration contributes towards COPD pathogenesis. Methods:The GEO database provided two human COPD gene expression datasets (GSE38974 and GSE76925; n=134) along with the relevant controls (n=49) for differentially expressed gene (DEG) analyses. Candidate biomarkers were identified using the support vector machine recursive feature elimination (SVM-RFE) analysis and the LASSO regression model. The discriminatory ability was determined using the area under the receiver operating characteristic curve (AUC) values. These candidate biomarkers were characterized in the GSE106986 dataset (14 COPD patients and 5 controls) in terms of their respective diagnostic values and expression levels. The CIBERSORT program was used to estimate patterns of tissue infiltration of 22 types of immune cells. Furthermore, the and model of COPD was established using cigarette smoke extract (CSE) to validated the bioinformatics results. Results:80 genes were identified DEG analysis that were primarily involved in cellular amino acid and metabolic processes, regulation of telomerase activity and phagocytosis, antigen processing and MHC class I-mediated peptide antigen presentation, and other biological processes. LASSO and SVM-RFE were used to further characterize the candidate diagnostic markers for COPD, SLC27A3, and STAU1. SLC27A3 and STAU1 were found to be diagnostic markers of COPD in the metadata cohort (AUC=0.734, AUC=0.745). Their relevance in COPD were validated in the GSE106986 dataset (AUC=0.900 AUC=0.971). Subsequent analysis of immune cell infiltration discovered an association between SLC27A3 and STAU1 with resting NK cells, plasma cells, eosinophils, activated mast cells, memory B cells, CD8+, CD4+, and helper follicular T-cells. The expressions of SLC27A3 and STAU1 were upregulated in COPD models both and . Immune infiltration activation was observed in COPD models, accompanied by the enhanced expression of SLC27A3 and STAU1. Whereas, the knockdown of SLC27A3 or STAU1 attenuated the effect of CSE on BEAS-2B cells. Conclusion:STUA1 and SLC27A3 are valuable diagnostic biomarkers of COPD. COPD pathogenesis is heavily influenced by patterns of immune cell infiltration. This study provides a molecular biology insight into COPD occurrence and in exploring new therapeutic means useful in COPD.
10.3389/fimmu.2022.740513
Analysis of Communal Molecular Mechanism Between Chronic Obstructive Pulmonary Disease and Osteoporosis.
International journal of chronic obstructive pulmonary disease
Background:Chronic obstructive pulmonary disease (COPD) patients with osteoporosis (OP) usually experience more frequent exacerbations, worse quality of life, and heavier economic burden, however, few studies have investigated common molecular mechanisms of COPD and OP. Objective:To explore the relationship between COPD and OP through bioinformatics analysis. Methods:The miRNA microarray data of COPD and OP were retrieved from the Gene Expression Database (GEO), and the differentially expressed microRNAs (DEmiRNAs) were screened and the intersection was obtained. The Targetscan, miRDB, and miRWalk databases were used to predict the target genes of DEmiRNA, and the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the R package clusterProfiler, the STRING database was used to analyze the target protein-protein interaction network (PPI) and screens to determine the core modules and core genes. Results:Two DEmiRNAs (miR-23a-5p, miR-194-3p) have been found in COPD and OP, which have predicted 76 and 114 target genes, respectively. GO functional annotations of miR-23a-5p were significantly enriched in CD40 signaling pathway, ubiquitin-conjugating enzyme activity, etc; KEGG pathways of miR-23a-5p were significantly enriched in ubiquitin-mediated proteolysis, folate biosynthesis, and regulation of actin cytoskeleton. GO function annotations of miR-194-3p were significantly enriched in T cell activation regulation, ubiquitin protein ligase activity, and DNA transcription factor binding; KEGG pathways of miR-194-3p were significantly enriched in cell adhesion molecules, intercellular tight junctions, and lysosomal pathway. PPI analysis found target coding proteins formed complex regulatory networks. Ten core genes (, , , , , , , , and ) were picked out among them, then we used the MCODE plugin found three core subnetworks. Conclusion:Two identical DEmiRNAs (miR-23a-5p, miR-194-3p) exist in the peripheral blood of COPD and OP patients, which are important biomarkers for COPD patients with OP and may represent novel targets for diagnosis and treatment of COPD patients with OP.
10.2147/COPD.S395492
Bioinformatics analysis of PLA2G7 as an immune-related biomarker in COPD by promoting expansion and suppressive functions of MDSCs.
International immunopharmacology
BACKGROUND:Immune mechanism is involved in the pathogenesis of chronic obstructive pulmonary disease (COPD). However, the exact immune pathogenesis still remains unclear. This study aimed to identify the immune-related biomarkers in COPD through bioinformatics analysis and its potential molecular mechanism. METHODS:GSE76925 was downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened, and enrichment analysis was performed. Single sample gene enrichment analysis (ssGSEA) was conducted to score the infiltration levels of immune cells. Weighted gene co-expression network analysis (WGCNA) was applied to identify trait-related modules and to further determine the key module-related DEGs. Moreover, the correlations between the key genes and clinical parameters and infiltration levels of immune cells were analyzed. Furthermore, expression of the selected one key gene, PLA2G7, the frequency of MDSCs, and the expression of MDSCs-related immunosuppressive mediators were determined among healthy, smokers and COPD patients. Finally, effects of PLA2G7 abnormal expression on the frequency of MDSCs and the expression of MDSCs-related immunosuppressive mediators were examined. RESULTS:A total of 352 DEGs were observed. These DEGs were mainly related to RNA metabolism and positive regulation of organelle organization. In addition, the black module was the most correlated with COPD. Six key genes (ADAMDEC1, CCL19, CHIT1, MMP9, PLA2G7, and TM4SF19) were identified between the black module and DEGs. Serum Lp-PLA2 and mRNA levels of PLA2G7, MDSCs, and MDSCs-related immunosuppressive mediators were found to be upregulated in COPD patients compared to the controls. The expression of PLA2G7 represented positive impact on the frequency of MDSCs and the expression of MDSCs-related immunosuppressive mediators. CONCLUSION:PLA2G7 may serve as a potential immune-related biomarker contributing to the progression of COPD by promoting expansion and suppressive functions of MDSCs.
10.1016/j.intimp.2023.110399
Contribution of cuproptosis and Cu metabolism-associated genes to chronic obstructive pulmonary disease.
Journal of cellular and molecular medicine
Airway epithelial cell injury plays a crucial role in the pathogenesis of chronic obstructive pulmonary disease (COPD). However, a novel form of Cu-induced programmed cell death known as cuproptosis has not yet been thoroughly investigated in the context of COPD. Clinical reports have suggested that high copper exposure may increase the risk of COPD. In this study, we aimed to determine the expression and potential functions of cuproptosis-related genes and genes associated with copper metabolism in COPD. We initially identified 52 copper metabolism-related genes based on a review of the literature. Subsequently, we calculated the expression levels of these genes using data from four GEO datasets. To gain insights into the activated signalling pathways and underlying mechanisms in COPD patients, we conducted Gene Ontology (GO) and KEGG pathway analyses, examined protein-protein interactions, and performed weighted correlation network analysis. Our findings revealed that 18 key copper metabolism-related genes, including 5 cuproptosis-related genes, were significantly enriched in signalling pathways and biological processes associated with the development of COPD. Further analysis of clinical data and animal experiments confirmed the high expression of certain cuproptosis key regulators, such as DLD and CDKN2A, in both healthy smokers and COPD smokers. Additionally, these regulators exhibited abnormal expression in a COPD rat model. Notably, copper content was found to be elevated in the lung tissues of COPD rats, suggesting its potential involvement in cuproptosis. These findings provide an experimental foundation for further research into the role of cuproptosis in COPD. Targeting copper metabolism-related genes may represent an effective approach for the treatment of COPD.
10.1111/jcmm.17985
Interplay of chronic obstructive pulmonary disease and colorectal cancer development: unravelling the mediating role of fatty acids through a comprehensive multi-omics analysis.
Journal of translational medicine
BACKGROUND:Chronic obstructive pulmonary disease (COPD) patients often exhibit gastrointestinal symptoms, A potential association between COPD and Colorectal Cancer (CRC) has been indicated, warranting further examination. METHODS:In this study, we collected COPD and CRC data from the National Health and Nutrition Examination Survey, genome-wide association studies, and RNA sequence for a comprehensive analysis. We used weighted logistic regression to explore the association between COPD and CRC incidence risk. Mendelian randomization analysis was performed to assess the causal relationship between COPD and CRC, and cross-phenotype meta-analysis was conducted to pinpoint crucial loci. Multivariable mendelian randomization was used to uncover mediating factors connecting the two diseases. Our results were validated using both NHANES and GEO databases. RESULTS:In our analysis of the NHANES dataset, we identified COPD as a significant contributing factor to CRC development. MR analysis revealed that COPD increased the risk of CRC onset and progression (OR: 1.16, 95% CI 1.01-1.36). Cross-phenotype meta-analysis identified four critical genes associated with both CRC and COPD. Multivariable Mendelian randomization suggested body fat percentage, omega-3, omega-6, and the omega-3 to omega-6 ratio as potential mediating factors for both diseases, a finding consistent with the NHANES dataset. Further, the interrelation between fatty acid-related modules in COPD and CRC was demonstrated via weighted gene co-expression network analysis and Kyoto Encyclopedia of Genes and Genomes enrichment results using RNA expression data. CONCLUSIONS:This study provides novel insights into the interplay between COPD and CRC, highlighting the potential impact of COPD on the development of CRC. The identification of shared genes and mediating factors related to fatty acid metabolism deepens our understanding of the underlying mechanisms connecting these two diseases.
10.1186/s12967-023-04278-1
Integration of genomics and transcriptomics predicts diabetic retinopathy susceptibility genes.
Skol Andrew D,Jung Segun C,Sokovic Ana Marija,Chen Siquan,Fazal Sarah,Sosina Olukayode,Borkar Poulami P,Lin Amy,Sverdlov Maria,Cao Dingcai,Swaroop Anand,Bebu Ionut, ,Stranger Barbara E,Grassi Michael A
eLife
We determined differential gene expression in response to high glucose in lymphoblastoid cell lines derived from matched individuals with type 1 diabetes with and without retinopathy. Those genes exhibiting the largest difference in glucose response were assessed for association with diabetic retinopathy in a genome-wide association study meta-analysis. Expression quantitative trait loci (eQTLs) of the glucose response genes were tested for association with diabetic retinopathy. We detected an enrichment of the eQTLs from the glucose response genes among small association p-values and identified folliculin () as a susceptibility gene for diabetic retinopathy. Expression of in response to glucose was greater in individuals with diabetic retinopathy. Independent cohorts of individuals with diabetes revealed an association of eQTLs with diabetic retinopathy. Mendelian randomization confirmed a direct positive effect of increased expression on retinopathy. Integrating genetic association with gene expression implicated as a disease gene for diabetic retinopathy.
10.7554/eLife.59980
The Shared Mechanism and Candidate Drugs of Multiple Sclerosis and Sjögren's Syndrome Analyzed by Bioinformatics Based on GWAS and Transcriptome Data.
Frontiers in immunology
Objective:This study aimed to explore the shared mechanism and candidate drugs of multiple sclerosis (MS) and Sjögren's syndrome (SS). Methods:MS- and SS-related susceptibility genes and differentially expressed genes (DEGs) were identified by bioinformatics analysis based on genome-wide association studies (GWAS) and transcriptome data from GWAS catalog and Gene Expression Omnibus (GEO) database. Pathway enrichment, Gene Ontology (GO) analysis, and protein-protein interaction analysis for susceptibility genes and DEGs were performed. The drugs targeting common pathways/genes were obtained through Comparative Toxicogenomics Database (CTD), DrugBank database, and Drug-Gene Interaction (DGI) Database. The target genes of approved/investigational drugs for MS and SS were obtained through DrugBank and compared with the common susceptibility genes. Results:Based on GWAS data, we found 14 hub common susceptibility genes (, , , , , , , , , , , , , and ), with 8 drugs targeting two or more than two genes, and 28 common susceptibility pathways, with 15 drugs targeting three or more than three pathways. Based on transcriptome data, we found 3 hub common DEGs (, , ) with 3 drugs and 10 common risk pathways with 435 drugs. "JAK-STAT signaling pathway" was included in common susceptibility pathways and common risk pathways at the same time. There were 133 overlaps including JAK-STAT inhibitors between agents from GWAS and transcriptome data. Besides, we found that and , identified as hub common susceptibility genes, were the targets of daclizumab and glatiramer that were used for MS, indicating that daclizumab and glatiramer may be therapeutic for SS. Conclusion:We observed the shared mechanism of MS and SS, in which JAK-STAT signaling pathway played a vital role, which may be the genetic and molecular bases of comorbidity of MS with SS. Moreover, JAK-STAT inhibitors were potential therapies for MS and SS, especially for their comorbidity.
10.3389/fimmu.2022.857014
Identification and Clinical Validation of Key Extracellular Proteins as the Potential Biomarkers in Relapsing-Remitting Multiple Sclerosis.
Li Meng,Chen Hongping,Yin Pengqi,Song Jihe,Jiang Fangchao,Tang Zhanbin,Fan Xuehui,Xu Chen,Wang Yingju,Xue Yang,Han Baichao,Wang Haining,Li Guozhong,Zhong Di
Frontiers in immunology
Background:Multiple sclerosis (MS) is a demyelinating disease of the central nervous system (CNS) mediated by autoimmunity. No objective clinical indicators are available for the diagnosis and prognosis of MS. Extracellular proteins are most glycosylated and likely to enter into the body fluid to serve as potential biomarkers. Our work will contribute to the in-depth study of the functions of extracellular proteins and the discovery of disease biomarkers. Methods:MS expression profiling data of the human brain was downloaded from the Gene Expression Omnibus (GEO). Extracellular protein-differentially expressed genes (EP-DEGs) were screened by protein annotation databases. GO and KEGG were used to analyze the function and pathway of EP-DEGs. STRING, Cytoscape, MCODE and Cytohubba were used to construct a protein-protein interaction (PPI) network and screen key EP-DEGs. Key EP-DEGs levels were detected in the CSF of MS patients. ROC curve and survival analysis were used to evaluate the diagnostic and prognostic ability of key EP-DEGs. Results:We screened 133 EP-DEGs from DEGs. EP-DEGs were enriched in the collagen-containing extracellular matrix, signaling receptor activator activity, immune-related pathways, and PI3K-Akt signaling pathway. The PPI network of EP-DEGs had 85 nodes and 185 edges. We identified 4 key extracellular proteins IL17A, IL2, CD44, IGF1, and 16 extracellular proteins that interacted with IL17A. We clinically verified that IL17A levels decreased, but Del-1 and resolvinD1 levels increased. The diagnostic accuracy of Del-1 (AUC: 0.947) was superior to that of IgG (AUC: 0.740) with a sensitivity of 82.4% and a specificity of 100%. High Del-1 levels were significantly associated with better relapse-free and progression-free survival. Conclusion:IL17A, IL2, CD44, and IGF1 may be key extracellular proteins in the pathogenesis of MS. IL17A, Del-1, and resolvinD1 may co-regulate the development of MS and Del-1 is a potential biomarker of MS. We used bioinformatics methods to explore the biomarkers of MS and validated the results in clinical samples. The study provides a theoretical and experimental basis for revealing the pathogenesis of MS and improving the diagnosis and prognosis of MS.
10.3389/fimmu.2021.753929
Identification and validation of diagnostic biomarkers of coronary artery disease progression in type 1 diabetes via integrated computational and bioinformatics strategies.
Computers in biology and medicine
OBJECTIVE:Our study aimed to identify early peripheral blood diagnostic biomarkers and elucidate the immune mechanisms of coronary artery disease (CAD) progression in patients with type 1 diabetes mellitus (T1DM). METHODS:Three transcriptome datasets were retrieved from the Gene Expression Omnibus (GEO) database. Gene modules associated with T1DM were selected with weighted gene co-expression network analysis. Differentially expressed genes (DEGs) between CAD and acute myocardial infarction (AMI) peripheral blood tissues were identified using limma. Candidate biomarkers were selected with functional enrichment analysis, node gene selection from a constructed protein-protein interaction (PPI) network, and 3 machine learning algorithms. Candidate expression was compared, and the receiver operating characteristic curve (ROC) and nomogram were constructed. Immune cell infiltration was assessed with the CIBERSORT algorithm. RESULTS:A total of 1283 genes comprising 2 modules were detected as the most associated with T1DM. In addition, 451 DEGs related to CAD progression were identified. Among them, 182 were common to both diseases and mainly enriched in immune and inflammatory response regulation. The PPI network yielded 30 top node genes, and 6 were selected using the 3 machine learning algorithms. Upon validation, 4 genes (TLR2, CLEC4D, IL1R2, and NLRC4) were recognized as diagnostic biomarkers with the area under the curve (AUC) > 0.7. All 4 genes were positively correlated with neutrophils in patients with AMI. CONCLUSION:We identified 4 peripheral blood biomarkers and provided a nomogram for early diagnosing CAD progression to AMI in patients with T1DM. The biomarkers were positively associated with neutrophils, indicating potential therapeutic targets.
10.1016/j.compbiomed.2023.106940
Identification of key interferon-stimulated genes for indicating the condition of patients with systemic lupus erythematosus.
Frontiers in immunology
Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with highly heterogeneous clinical symptoms and severity. There is complex pathogenesis of SLE, one of which is IFNs overproduction and downstream IFN-stimulated genes (ISGs) upregulation. Identifying the key ISGs differentially expressed in peripheral blood mononuclear cells (PBMCs) of patients with SLE and healthy people could help to further understand the role of the IFN pathway in SLE and discover potential diagnostic biomarkers. The differentially expressed ISGs (DEISG) in PBMCs of SLE patients and healthy persons were screened from two datasets of the Gene Expression Omnibus (GEO) database. A total of 67 DEISGs, including 6 long noncoding RNAs (lncRNAs) and 61 messenger RNAs (mRNAs) were identified by the "DESeq2" R package. According to Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, those DEISGs were mainly concentrated in the response to virus and immune system processes. Protein-protein interaction (PPI) network showed that most of these DEISGs could interact strongly with each other. Then, IFIT1, RSAD2, IFIT3, USP18, ISG15, OASL, MX1, OAS2, OAS3, and IFI44 were considered to be hub ISGs in SLE by "MCODE" and "Cytohubba" plugins of Cytoscape, Moreover, the results of expression correlation suggested that 3 lncRNAs (NRIR, FAM225A, and LY6E-DT) were closely related to the IFN pathway. The lncRNA NRIR and mRNAs (RSAD2, USP18, IFI44, and ISG15) were selected as candidate ISGs for verification. RT-qPCR results showed that PBMCs from SLE patients had substantially higher expression levels of 5 ISGs compared to healthy controls (HCs). Additionally, statistical analyses revealed that the expression levels of these ISGs were strongly associated to various clinical symptoms, including thrombocytopenia and facial erythema, as well as laboratory indications, including the white blood cell (WBC) count and levels of autoantibodies. The Receiver Operating Characteristic (ROC) curve demonstrated that the IFI44, USP18, RSAD2, and IFN score had good diagnostic capabilities of SLE. According to our study, SLE was associated with ISGs including NRIR, RSAD2, USP18, IFI44, and ISG15, which may contribute to the future diagnosis and new personalized targeted therapies.
10.3389/fimmu.2022.962393
Three hematologic/immune system-specific expressed genes are considered as the potential biomarkers for the diagnosis of early rheumatoid arthritis through bioinformatics analysis.
Cheng Qi,Chen Xin,Wu Huaxiang,Du Yan
Journal of translational medicine
BACKGROUND:Rheumatoid arthritis (RA) is the most common chronic autoimmune connective tissue disease. However, early RA is difficult to diagnose due to the lack of effective biomarkers. This study aimed to identify new biomarkers and mechanisms for RA disease progression at the transcriptome level through a combination of microarray and bioinformatics analyses. METHODS:Microarray datasets for synovial tissue in RA or osteoarthritis (OA) were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified by R software. Tissue/organ-specific genes were recognized by BioGPS. Enrichment analyses were performed and protein-protein interaction (PPI) networks were constructed to understand the functions and enriched pathways of DEGs and to identify hub genes. Cytoscape was used to construct the co-expressed network and competitive endogenous RNA (ceRNA) networks. Biomarkers with high diagnostic value for the early diagnosis of RA were validated by GEO datasets. The ggpubr package was used to perform statistical analyses with Student's t-test. RESULTS:A total of 275 DEGs were identified between 16 RA samples and 10 OA samples from the datasets GSE77298 and GSE82107. Among these DEGs, 71 tissue/organ-specific expressed genes were recognized. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that DEGs are mostly enriched in immune response, immune-related biological process, immune system, and cytokine signal pathways. Fifteen hub genes and gene cluster modules were identified by Cytoscape. Eight haematologic/immune system-specific expressed hub genes were verified by GEO datasets. GZMA, PRC1, and TTK may be potential biomarkers for diagnosis of early RA. NEAT1-miR-212-3p/miR-132-3p/miR-129-5p-TTK, XIST-miR-25-3p/miR-129-5p-GZMA, and TTK_hsa_circ_0077158- miR-212-3p/miR-132-3p/miR-129-5p-TTK might be potential RNA regulatory pathways to regulate the disease progression of early RA. CONCLUSIONS:This work identified three haematologic/immune system-specific expressed genes, namely, GZMA, PRC1, and TTK, as potential biomarkers for the early diagnosis and treatment of RA and provided insight into the mechanisms of disease development in RA at the transcriptome level. In addition, we proposed that NEAT1-miR-212-3p/miR-132-3p/miR-129-5p-TTK, XIST-miR-25-3p/miR-129-5p-GZMA, and TTK_hsa_circ_0077158-miR-212-3p/miR-132-3p/miR-129-5p-TTK are potential RNA regulatory pathways that control disease progression in early RA.
10.1186/s12967-020-02689-y
Expression characteristics of interferon-stimulated genes and possible regulatory mechanisms in lupus patients using transcriptomics analyses.
Deng Yiyao,Zheng Ying,Li Delun,Hong Quan,Zhang Min,Li Qinggang,Fu Bo,Wu Lingling,Wang Xu,Shen Wanjun,Zhang Yingjie,Chang Jiakai,Song Kangkang,Liu Xiaomin,Shang Shunlai,Cai Guangyan,Chen Xiangmei
EBioMedicine
BACKGROUND:Type I interferon signature is one of the most important features of systemic lupus erythematosus (SLE), which indicates an active immune response to antigen invasion. Characteristics of type I interferon-stimulated genes (ISGs) in SLE patients have not been well described thus far. METHODS:We analyzed 35,842 cells of PBMC single-cell RNA sequencing data of five SLE patients and three healthy controls. Thereafter, 178 type I ISGs among DEGs of all cell clusters were screened based on the Interferome Database and AUCell package was used for ISGs activity calculation. To determine whether common ISG features exist in PBMCs and kidneys of patients with SLE, we analyzed kidney transcriptomic data from patients with lupus nephritis (LN) from the GEO database. MRL/lpr mice model were used to verify our findings. FINDINGS:We found that monocytes, B cells, dendritic cells, and granulocytes were significantly increased in SLE patients, while subsets of T cells were significantly decreased. Neutrophils and low-density granulocytes (LDGs) exhibited the highest ISG activity. GO and pathway enrichment analyses showed that DEGs focused on leukocyte activation, cell secretion, and pathogen infection. Thirty-one common ISGs were found expressed in both PBMCs and kidneys; these ISGs were also most active in neutrophils and LDGs. Transcription factors including PLSCR1, TCF4, IRF9 and STAT1 were found to be associated to ISGs expression. Consistently, we found granulocyte infiltration in the kidneys of MRL/lpr mice. Granulocyte inhibitor Avacopan reduced granulocyte infiltration and reversed renal conditions in MRL/lpr mice. INTERPRETATION:This study shows for the first time, the use of the AUCell method to describe ISG activity of granulocytes in SLE patients. Moreover, Avacopan may serve as a granulocyte inhibitor for treatment of lupus patients in the future. FUNDING:None.
10.1016/j.ebiom.2021.103477
Exploration of the Shared Gene Signatures and Molecular Mechanisms Between Systemic Lupus Erythematosus and Pulmonary Arterial Hypertension: Evidence From Transcriptome Data.
Yao Menghui,Zhang Chunyi,Gao Congcong,Wang Qianqian,Dai Mengmeng,Yue Runzhi,Sun Wenbo,Liang Wenfang,Zheng Zhaohui
Frontiers in immunology
Background:Systemic lupus erythematosus (SLE) is an autoimmune disease that can affect multiple systems. Pulmonary arterial hypertension (PAH) has a close linkage with SLE. However, the inter-relational mechanisms between them are still unclear. This article aimed to explore the shared gene signatures and potential molecular mechanisms in SLE and PAH. Methods:The microarray data of SLE and PAH in the Gene Expression Omnibus (GEO) database were downloaded. The Weighted Gene Co-Expression Network Analysis (WGCNA) was used to identify the co-expression modules related to SLE and PAH. The shared genes existing in the SLE and PAH were performed an enrichment analysis by ClueGO software, and their unique genes were also performed with biological processes analyses using the DAVID website. The results were validated in another cohort by differential gene analysis. Moreover, the common microRNAs (miRNAs) in SLE and PAH were obtained from the Human microRNA Disease Database (HMDD) and the target genes of whom were predicted through the miRTarbase. Finally, we constructed the common miRNAs-mRNAs network with the overlapped genes in target and shared genes. Results:Using WGCNA, four modules and one module were identified as the significant modules with SLE and PAH, respectively. A ClueGO enrichment analysis of shared genes reported that highly activated type I IFN response was a common feature in the pathophysiology of SLE and PAH. The results of differential analysis in another cohort were extremely similar to them. We also proposed a disease road model for the possible mechanism of PAH secondary to SLE according to the shared and unique gene signatures in SLE and PAH. The miRNA-mRNA network showed that hsa-miR-146a might regulate the shared IFN-induced genes, which might play an important role in PAH secondary to SLE. Conclusion:Our work firstly revealed the high IFN response in SLE patients might be a crucial susceptible factor for PAH and identified novel gene candidates that could be used as biomarkers or potential therapeutic targets.
10.3389/fimmu.2021.658341
Transcriptomic analysis reveals the potential crosstalk genes and immune relationship between IgA nephropathy and periodontitis.
Frontiers in immunology
Background:It is well known that periodontitis has an important impact on systemic diseases. The aim of this study was to investigate potential crosstalk genes, pathways and immune cells between periodontitis and IgA nephropathy (IgAN). Methods:We downloaded periodontitis and IgAN data from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were used to identify shared genes. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the shared genes. Hub genes were further screened using least absolute shrinkage and selection operator (LASSO) regression, and a receiver operating characteristic (ROC) curve was drawn according to the screening results. Finally, single-sample GSEA (ssGSEA) was used to analyze the infiltration level of 28 immune cells in the expression profile and its relationship with shared hub genes. Results:By taking the intersection of WGCNA important module genes and DEGs, we found that the and genes were the most important cross-talk genes between periodontitis and IgAN. GO analysis showed that the shard genes were most significantly enriched in kinase regulator activity. The LASSO analysis results showed that two overlapping genes ( and ) were the optimal shared diagnostic biomarkers for periodontitis and IgAN. The immune infiltration results revealed that T cells and B cells play an important role in the pathogenesis of periodontitis and IgAN. Conclusion:This study is the first to use bioinformatics tools to explore the close genetic relationship between periodontitis and IgAN. The and genes were the most important cross-talk genes between periodontitis and IgAN. T-cell and B-cell-driven immune responses may play an important role in the association between periodontitis and IgAN.
10.3389/fimmu.2023.1062590
Therapeutic Effects of Naringin in Rheumatoid Arthritis: Network Pharmacology and Experimental Validation.
Aihaiti Yirixiati,Song Cai Yong,Tuerhong Xiadiye,Ni Yang Yan,Ma Yao,Shi Zheng Hai,Xu Ke,Xu Peng
Frontiers in pharmacology
Rheumatoid arthritis is a chronic autoimmune disease characterized by persistent hyperplasia of the synovial membrane and progressive erosion of articular cartilage. Disequilibrium between the proliferation and death of RA fibroblast-like synoviocytes (RA-FLSs) is the critical factor in progression of RA. Naringin has been reported to exert anti-inflammatory and antioxidant effect in acute and chronic animal models of RA. However, the therapeutic effect and underlying mechanisms of naringin in human RA-FLS remain unclear. Based on network pharmacology, the corresponding targets of naringin were identified using SwissTargetPrediction database, STITCH database, and Comparative Toxicogenomics Database. Deferentially expressed genes (DEGs) in RA were obtained from the GEO database. The protein-protein interaction (PPI) networks of intersected targets were constructed using the STRING database and visualized using Cytoscape. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, and the pathways directly related to pathogenesis of RA were integrated manually. Further, studies were carried out based on network pharmacology. 99 target genes were intersected between targets of naringin and DEGs. The PPI network and topological analysis indicated that IL-6, MAPK8, MMP-9, TNF, and MAPK1 shared the highest centrality among all. GO analysis and KEGG analysis indicated that target genes were mostly enriched in (hsa05200) pathways in cancer, (hsa05161) hepatitis B, (hsa04380) osteoclast differentiation, (hsa04151) PI3K-Akt signaling pathway, and (hsa05142) Chagas disease (American trypanosomiasis). studies revealed that naringin exposure was found to promote apoptosis of RA-FLS, increased the activation of caspase-3, and increased the ratio of Bax/Bcl-2 in a dose-dependent manner. Furthermore, treatment of naringin attenuated the production of inflammatory cytokines and matrix metalloproteinases (MMPs) in TNF-ɑ-induced RA-FLS. Moreover, treatment of naringin inhibited the phosphorylation of Akt and ERK in RA-FLS. Network pharmacology provides a predicative strategy to investigate the therapeutic effects and mechanisms of herbs and compounds. Naringin inhibits inflammation and MMPs production and promotes apoptosis in RA-FLS PI3K/Akt and MAPK/ERK signaling pathways.
10.3389/fphar.2021.672054
Potential diagnostic markers and therapeutic targets for rheumatoid arthritis with comorbid depression based on bioinformatics analysis.
Frontiers in immunology
Background:Rheumatoid arthritis (RA) and depression are prevalent diseases that have a negative impact on the quality of life and place a significant economic burden on society. There is increasing evidence that the two diseases are closely related, which could make the disease outcomes worse. In this study, we aimed to identify diagnostic markers and analyzed the therapeutic potential of key genes. Methods:We assessed the differentially expressed genes (DEGs) specific for RA and Major depressive disorder (MDD) and used weighted gene co-expression network analysis (WGCNA) to identify co-expressed gene modules by obtaining the Gene expression profile data from Gene Expression Omnibus (GEO) database. By using the STRING database, a protein-protein interaction (PPI) network constructed and identified key genes. We also employed two types of machine learning techniques to derive diagnostic markers, which were assessed for their association with immune cells and potential therapeutic effects. Molecular docking and experiments were used to validate these analytical results. Results:In total, 48 DEGs were identified in RA with comorbid MDD. The PPI network was combined with WGCNA to identify 26 key genes of RA with comorbid MDD. Machine learning-based methods indicated that RA combined with MDD is likely related to six diagnostic markers: , , , , , and . and are closely associated with diverse immune cells in RA. However, apart from , the expression levels of the other five genes were associated with the composition of the majority of immune cells in MDD. Molecular docking and studies have revealed that Aucubin (AU) exerts the therapeutic effect through the downregulation of and gene expression in PC12 cells. Conclusion:Our study indicates that six diagnostic markers were the basis of the comorbidity mechanism of RA and MDD and may also be potential therapeutic targets. Further mechanistic studies of the pathogenesis and treatment of RA and MDD may be able to identify new targets using these shared pathways.
10.3389/fimmu.2023.1007624
Identification of immune-related genes in diagnosing atherosclerosis with rheumatoid arthritis through bioinformatics analysis and machine learning.
Frontiers in immunology
Background:Increasing evidence has proven that rheumatoid arthritis (RA) can aggravate atherosclerosis (AS), and we aimed to explore potential diagnostic genes for patients with AS and RA. Methods:We obtained the data from public databases, including Gene Expression Omnibus (GEO) and STRING, and obtained the differentially expressed genes (DEGs) and module genes with Limma and weighted gene co-expression network analysis (WGCNA). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis, the protein-protein interaction (PPI) network, and machine learning algorithms [least absolute shrinkage and selection operator (LASSO) regression and random forest] were performed to explore the immune-related hub genes. We used a nomogram and receiver operating characteristic (ROC) curve to assess the diagnostic efficacy, which has been validated with GSE55235 and GSE73754. Finally, immune infiltration was developed in AS. Results:The AS dataset included 5,322 DEGs, while there were 1,439 DEGs and 206 module genes in RA. The intersection of DEGs for AS and crucial genes for RA was 53, which were involved in immunity. After the PPI network and machine learning construction, six hub genes were used for the construction of a nomogram and for diagnostic efficacy assessment, which showed great diagnostic value (area under the curve from 0.723 to 1). Immune infiltration also revealed the disorder of immunocytes. Conclusion:Six immune-related hub genes (NFIL3, EED, GRK2, MAP3K11, RMI1, and TPST1) were recognized, and the nomogram was developed for AS with RA diagnosis.
10.3389/fimmu.2023.1126647
, and Affect the Development of Chronic Obstructive Pulmonary Disease.
Yang Danlei,Yan Ying,Hu Fen,Wang Tao
International journal of chronic obstructive pulmonary disease
Purpose:Chronic obstructive pulmonary disease (COPD) is a progressive lung disease characterized by poor airflow. The purpose of this study was to explore the mechanisms involved in the development of COPD. Patients and Methods:The mRNA expression profile GSE100281, consisting of 79 COPD and 16 healthy samples, was acquired from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between COPD samples and healthy samples were analyzed using the limma package. Functional enrichment analysis for the DEGs was carried out using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) tool. Furthermore, DEG-compound pairs were predicted using the Comparative Toxicogenomics Database. The KEGG metabolite IDs corresponding to the compounds were also obtained through the MetaboAnalyst pipeline. Based on the diffusion algorithm, the metabolite network was constructed. Finally, the expression levels of key genes were determined using quantitative PCR (qPCR). Results:There were 594 DEGs identified between the COPD and healthy samples, including 242 upregulated and 352 downregulated genes. A total of 696 DEG-compound pairs, such as -C00469 (ethanol) and -C00389 (quercetin) pairs, were predicted. , and were included in the top 10 DEG-compound pairs. Additionally, 57 metabolites were obtained. In particular, hsa04750 (inflammatory mediator regulation of TRP channels)-C00469 (ethanol) and hsa04152 (AMPK signaling pathway)-C00389 (quercetin) pairs were found in the metabolite network. The results of qPCR showed that the expression of , and was consistent with that predicted using bioinformatic analysis. Conclusion:, and may play important functions in the development and progression of COPD.
10.2147/COPD.S220675
Quercetin is a Potential Therapy for Rheumatoid Arthritis via Targeting Caspase-8 Through Ferroptosis and Pyroptosis.
Journal of inflammation research
Background:Rheumatoid arthritis (RA) is one of the most common chronic inflammatory autoimmune diseases. However, the underlying molecular mechanisms of its pathogenesis are unknown. This study aimed to identify the common biomarkers of ferroptosis and pyroptosis in RA and screen potential drugs. Methods:The RA-related differentially expressed genes (DEGs) in GSE55235 were screened by R software and intersected with ferroptosis and pyroptosis gene libraries to obtain differentially expressed ferroptosis-related genes (DEFRGs) and differentially expressed pyroptosis-related genes (DEPRGs). We performed Gene Ontology (GO), Kyoto Encyclopedia of the Genome (KEGG), ClueGO, and Protein-Protein Interaction (PPI) analysis for DEFRGs and DEPRGs and validated them by machine learning. The microRNA/transcription factor (TF)-hub genes regulatory network was further constructed. The key gene was validated using the GSE77298 validation set, cellular validation was performed in in vitro experiments, and immune infiltration analysis was performed using CIBERSORT. Network pharmacology was used to find key gene-targeting drugs, followed by molecular docking and molecular dynamics simulations to analyze the binding stability between small-molecule drugs and large-molecule proteins. Results:Three hub genes (CASP8, PTGS2, and JUN) were screened via bioinformatics, and the key gene (CASP8) was validated and obtained through the validation set, and the diagnostic efficacy was verified to be excellent through the receiver operating characteristic (ROC) curves. The ferroptosis and pyroptosis phenotypes were constructed by fibroblast-like synoviocytes (FLS), and caspase-8 was detected and validated as a common biomarker for ferroptosis and pyroptosis in RA, and quercetin can reduce caspase-8 levels. Quercetin was found to be a potential target drug for caspase-8 by network pharmacology, and the stability of their binding was further verified using molecular docking and molecular dynamics simulations. Conclusion:Caspase-8 is an important biomarker for ferroptosis and pyroptosis in RA, and quercetin is a potential therapy for RA via targeting caspase-8 through ferroptosis and pyroptosis.
10.2147/JIR.S439494
System analysis based on the pyroptosis-related genes identifes GSDMD as a novel therapy target for skin cutaneous melanoma.
Journal of translational medicine
BACKGROUND:Skin cutaneous melanoma (SKCM) is the most aggressive skin cancer, accounting for more than 75% mortality rate of skin-related cancers. As a newly identified programmed cell death, pyroptosis has been found to be closely associated with tumor progression. Nevertheless, the prognostic significance of pyroptosis in SKCM remains elusive. METHODS:A total of 469 SKCM samples and 812 normal samples were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Firstly, differentially expressed pyroptosis-related genes (PRGs) between normal samples and SKCM samples were identified. Secondly, we established a prognostic model based on univariate Cox and LASSO Cox regression analyses, which was validated in the test cohort from GSE65904. Thirdly, a nomogram was used to predict the survival probability of SKCM patients. The R package "pRRophetic" was utilized to identify the drug sensitivity between the low- and high-risk groups. Tumor immune infiltration was evaluated using "immuneeconv" R package. Finally, the function of GSDMD and SB525334 was explored in A375 and A2058 cells. RESULTS:Based on univariate Cox and LASSO regression analyses, we established a prognostic model with identified eight PRGs (AIM2, CASP3, GSDMA, GSDMC, GSDMD, IL18, NLRP3, and NOD2), which was validated in the test cohort. SKCM patients were divided into low- and high-risk groups based on the median of risk score. Kaplan-Meier survival analysis showed that high-risk patients had shorter overall survival than low-risk patients. Additionally, time-dependent ROC curves validated the accuracy of the risk model in predicting the prognosis of SKCM. More importantly, 4 small molecular compounds (SB525334, SR8278, Gemcitabine, AT13387) were identified, which might be potential drugs for patients in different risk groups. Finally, overexpression of GSDMD and SB525334 treatment inhibit the proliferation, migration, and invasion of SKCM cells. CONCLUSION:In this study, we constructed a prognostic model based on PRGs and identified GSDMD as a potential therapeutic target, which provide new insights into SKCM treatment.
10.1186/s12967-023-04513-9