PURPOSE:Systemic sclerosis-associated interstitial lung disease (SSc-ILD) is one of the most severe complications of systemic sclerosis (SSc) and is the leading cause of SSc-related deaths. However, the precise pathogenesis of pulmonary fibrosis in SSc-ILD remains unknown. This study aimed to evaluate the competing endogenous RNA (ceRNA) regulatory network and immune cell infiltration patterns in SSc-ILD. METHODS:One microRNA (miRNA) and three messenger RNA (mRNA) microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. Then, the differentially expressed miRNAs (DEmiRs) and mRNAs (DEMs) between SSc-ILD patients and normal controls were identified, respectively, followed by the prediction of the target genes and target lncRNAs of DEmiRs. The overlapping genes between DEmiRs target genes and DEMs were identified as core mRNAs to construct the ceRNA network. In addition, the "Cell Type Identification by Estimating Relative Subsets of Known RNA Transcripts (CIBERSORT)" algorithm was used to analyze the composition of infiltrating immune cells in lung tissues of SSc-ILD patients and controls, and differentially expressed immune cells were recognized. The correlation between immune cells and core mRNAs was evaluated by Pearson correlation analysis. RESULTS:Totally, 42 SSc-ILD lung tissues and 18 normal lung tissues were included in this study. We identified 35 DEmiRs and 142 DEMs and predicted 1,265 target genes of DEmiRs. Then, 9 core mRNAs related to SSc-ILD were recognized, which were the overlapping genes between DEmiRs target genes and DEMs. Meanwhile, 9 DEmiRs related to core mRNAs were identified reversely, and their target lncRNAs were predicted. In total, 9 DEmiRs, 9 core mRNAs, and 51 predicted lncRNAs were integrated to construct the ceRNA regulatory network of SSc-ILD. In addition, 9 types of immune cells were differentially expressed in lung tissues between SSc-ILD patients and controls. Some core mRNAs, such as , , and , were positively or negatively correlated with the number of infiltrating immune cells. CONCLUSION:This is the first comprehensive study to construct the potential ceRNA regulatory network and analyze the composition of infiltrating immune cells in lung tissues of SSc-ILD patients, which improves our understanding of the pathogenesis of SSc-ILD.

Chronic rhinosinusitis with nasal polyps (CRSwNP) refers to chronic inflammation of the sinonasal mucosa. It can either be eosinophilic (ECRSwNP) or non-eosinophilic (non-ECRSwNP). However, immune cell infiltration in the microenvironment and pathogenesis of ECRSwNP and non-ECRSwNP are still unclear. The aim of the present study was to assess the immune cell infiltration and molecular mechanisms of ECRSwNP and non-ECRSwNP. In the present study, 22 immune cell types in ECRSwNP and non-ECRSwNP were investigated by CIBERSORT based on transcriptome data. The core gene related pathophysiology of CRSwNP was analyzed using Weighted Gene Correlation Network Analysis according to the phenotype of the infiltrated eosinophils and nasal polyps (NP). A total of four types of immune cells (mast cells, activated dendritic cells, M2 macrophages and activated natural killer cells) were demonstrated to have a direct and indirect correlation with eosinophilic infiltration in ECRSwNP. M1 macrophages and activated CD4 memory T cells were correlated with major immune cell types in non-ECRSwNP. NP could affect the expression of 'olfactory receptor activity' and 'channel activity' genes to impair the olfactory signaling pathway and neuroactive ligand receptor pathway. 'Cell adhesion molecule binding', 'cytokine receptor binding' and 'glucocorticoid receptor binding' were significantly enriched in ECRSwNP, whereas epithelial cell injury, autophagy and the mTOR pathway (hsa04140 and hsa04150) may serve an important role in the pathogenesis of non-ECRSwNP. There were significantly different immune cell infiltration and related core genes expression characteristics between ECRSwNP and non-ECRSwNP. The results of the present study provide an improved basis for elucidation of the mechanism and treatment of CRSwNP.

Background:Low-grade chronic inflammation in dysfunctional adipose tissue links obesity with insulin resistance through the activation of tissue-infiltrating immune cells. Numerous studies have reported on the pathogenesis of insulin-resistance. However, few studies focused on genes from genomic database. In this study, we would like to explore the correlation of genes and immune cells infiltration in adipose tissue via comprehensive bioinformatics analyses and experimental validation in mice and human adipose tissue. Methods:Gene Expression Omnibus (GEO) datasets (GSE27951, GSE55200, and GSE26637) of insulin-resistant individuals or type 2 diabetes patients and normal controls were downloaded to get differently expressed genes (DEGs), and GO and KEGG pathway analyses were performed. Subsequently, we integrated DEGs from three datasets and constructed commonly expressed DEGs' PPI net-works across datasets. Center regulating module of DEGs and hub genes were screened through MCODE and cytoHubba in Cytoscape. Three most significant hub genes were further analyzed by GSEA analysis. Moreover, we verified the predicted hub genes by performing RT qPCR analysis in animals and human samples. Besides, the relative fraction of 22 immune cell types in adipose tissue was detected by using the deconvolution algorithm of CIBERSORT (Cell Type Identification by Estimating Relative Subsets of RNA Transcripts). Furthermore, based on the significantly changed types of immune cells, we performed correlation analysis between hub genes and immune cells. And, we performed immunohistochemistry and immunofluorescence analysis to verify that the hub genes were associated with adipose tissue macrophages (ATM). Results:Thirty DEGs were commonly expressed across three datasets, most of which were upregulated. DEGs mainly participated in the process of multiple immune cells' infiltration. In protein-protein interaction network, we identified , , and as hub genes. GSEA analysis suggested high expression of the three hub genes was correlated with immune cells functional pathway's activation. Immune cell infiltration and correlation analysis revealed that there were significant positive correlations between and M0 macrophages, and M0 macrophages, Plasma cells, and CD8 T cells. Finally, hub genes were associated with ATMs infiltration by experimental verification. Conclusions:This article revealed that , , and were potential hub genes associated with immune cells' infiltration and the function of proinflammation, especially adipose tissue macrophages, in the progression of obesity-induced diabetes or insulin-resistance.

Lung adenocarcinoma (LUAD) is a malignant tumor with high heterogeneity and poor prognosis. Ferroptosis, a form of regulated cell-death-related iron, has been proven to trigger inflammation-associated immunosuppression in the tumor microenvironment, which promotes tumor growth. Therefore, the clinical prognostic value of ferroptosis-related genes in LUAD needs to be further explored. In this study, we downloaded the mRNA expression profiles and corresponding clinical data of LUAD patients from the Cancer Genome Atlas database. The least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized to construct ferroptosis-related gene signature. Based on these, we established the nomograms for prognosis prediction and validated the model in the GSE72094 dataset. The cell type was identified using the CIBERSORT algorithm for estimating relative subsets of RNA transcripts, which was then used to screen significant tumor immune-infiltrating cells associated with the LUAD prognosis prediction model. Subsequently, we applied co-expression analysis to reveal the relationship between ferroptosis-related genes and significant immune cells. The univariate COX regression analysis showed that 20 genes were associated with the overall survival (OS) as prognostic differentially expressed genes (DEGs) (FDR <0.05). Patients were divided into two risk groups using a 13-gene signature, with the high-risk group having a significantly worse OS than their low-risk counterparts ( < 0.001). We used receiver operating characteristic (ROC) curve analysis to confirm the predictive capacity of the signature. Besides, we identified seven pairs of ferroptosis-related genes and tumor-infiltrating immune cells associated with the prognosis of LUAD patients. In this study, we construct a ferroptosis-related gene signature that can be used for prognostic prediction in LUAD. In addition, we reveal a potential connection between ferroptosis and tumor-infiltrating immune cells.

Rheumatoid arthritis (RA) is a systemic autoimmune disease whose principal pathological change is aggressive chronic synovial inflammation; however, the specific etiology and pathogenesis have not been fully elucidated. We downloaded the synovial tissue gene expression profiles of four human knees from the Gene Expression Omnibus database, analyzed the differentially expressed genes in the normal and RA groups, and assessed their enrichment in functions and pathways using bioinformatics methods and the STRING online database to establish protein-protein interaction networks. Cytoscape software was used to obtain 10 hub genes; receiver operating characteristic (ROC) curves were calculated for each hub gene and differential expression analysis of the two groups of hub genes. The CIBERSORT algorithm was used to impute immune infiltration. We identified the signaling pathways that play important roles in RA and 10 hub genes: Ccr1, Ccr2, Ccr5, Ccr7, Cxcl5, Cxcl6, Cxcl13, Ccl13, Adcy2, and Pnoc. The diagnostic value of these 10 hub genes for RA was confirmed using ROC curves and expression analysis. Adcy2, Cxcl13, and Ccr5 are strongly associated with RA development. The study also revealed that the differential infiltration profile of different inflammatory immune cells in the synovial tissue of RA is an extremely critical factor in RA progression. This study may contribute to the understanding of signaling pathways and biological processes associated with RA and the role of inflammatory immune infiltration in the pathogenesis of RA. In addition, this study shows that Adcy2, Cxcl13, and Ccr5 have the potential to be biomarkers for RA treatment.

Immune cell infiltration (ICI) plays a pivotal role in the development of diabetic nephropathy (DN). Evidence suggests that immune-related genes play an important role in the initiation of inflammation and the recruitment of immune cells. However, the underlying mechanisms and immune-related biomarkers in DN have not been elucidated. Therefore, this study aimed to explore immune-related biomarkers in DN and the underlying mechanisms using bioinformatic approaches. In this study, four DN glomerular datasets were downloaded, merged, and divided into training and test cohorts. First, we identified 55 differentially expressed immune-related genes; their biological functions were mainly enriched in leukocyte chemotaxis and neutrophil migration. The CIBERSORT algorithm was then used to evaluate the infiltrated immune cells; macrophages M1/M2, T cells CD8, and resting mast cells were strongly associated with DN. The ICI-related gene modules as well as 25 candidate hub genes were identified to construct a protein-protein interactive network and conduct molecular complex detection using the GOSemSim algorithm. Consequently, FN1, C3, and VEGFC were identified as immune-related biomarkers in DN, and a related transcription factor-miRNA-target network was constructed. Receiver operating characteristic curve analysis was estimated in the test cohort; FN1 and C3 had large area under the curve values (0.837 and 0.824, respectively). Clinical validation showed that FN1 and C3 were negatively related to the glomerular filtration rate in patients with DN. Six potential therapeutic small molecule compounds, such as calyculin, phenamil, and clofazimine, were discovered in the connectivity map. In conclusion, FN1 and C3 are immune-related biomarkers of DN.

BACKGROUND:Host immunity plays an important role against oral microorganisms in periodontitis. METHODS:This study assessed the infiltrating immune cell subtypes in 133 healthy periodontal and 210 chronic periodontitis tissues from Gene Expression Omnibus (GEO) datasets using the CIBERSORT gene signature files. RESULTS:Plasma cells, naive B cells and neutrophils were all elevated in periodontitis tissues, when compared to those in healthy controls. In contrast, memory B cells, resting dendritic, mast cells and CD4 memory cells, as well as activated mast cells, M1 and M2 macrophages, and follicular helper T cells, were mainly present in healthy periodontal tissues. Furthermore, these periodontitis tissues generally contained a higher proportion of activated CD4 memory T cells, while the other subtypes of T cells, including resting CD4 memory T cells, CD8 T cells, follicular helper T cells (T) and regulatory T cells (Tregs), were relatively lower in periodontitis tissues, when compared to healthy tissues. The ratio of dendritic and mast cells and macrophages was lower in periodontitis tissues, when compared to healthy tissues. In addition, there was a significant negative association of plasma cells with most of the other immune cells, such as plasma cells vs. memory B cells (γ = - 0.84), plasma cells vs. resting dendritic cells (γ = - 0.64), plasma cells vs. resting CD4 memory T cells (γ = 0.50), plasma cells versus activated dendritic cells (γ = - 0.46), plasma cells versus T (γ = - 0.46), plasma cells versus macrophage M2 cells (γ = - 0.43), or plasma cells versus macrophage M1 cells (γ = - 0.40), between healthy control and periodontitis tissues. CONCLUSION:Plasma cells, naive B cells and neutrophils were all elevated in periodontitis tissues. The infiltration of different immune cell subtypes in the periodontitis site could lead the host immunity against periodontitis.

Acne is a common inflammatory skin disease with the dysregulation of innate and adaptive immunity. However, the underlying mechanism of acne has not been completely elucidated. In this study, we identified gene signatures and the immune-related regulatory network in acne using integrated bioinformatics methods. Here, 303 Differentially expressed genes (DEGs) and 28 Hub genes were identified in acne (GSE53795 and GSE108110), which were associated with the inflammation-related signaling pathway. Subsequently, the CIBERSORT algorithm revealed the increased proinflammatory cells in acne. Moreover, we identified 3 kinases (FGR, HCK and LYN) and 2 transcription factors (TFs) (IRF8 and ZBTB16) from DEGs as the key genes, which regulated immune cell infiltration via targeting immune-related genes in acne. The upregulated 3 kinases (FGR, HCK and LYN) and IRF8, and the downregulated ZBTB16 were also confirmed in GSE6475 and in Acne mice. Based on the expression levels of these key genes, the tissues could be divided into 2 clusters using consensus cluster analysis. GSEA analysis showed that inflammation-related signaling pathways significantly enriched in cluster 2, indicating the important role of kinase and TFs on immune regulation in acne. Finally, we found that isotretinoin and trifarotene (CD5789) treatment repressed the expression of immune genes but not the expression of the kinases and TFs, indicating that kinases and TFs may be novel therapeutic target for acne. In conclusion, 3 kinases and 2 TFs were identified and validated as key regulators in the immune-related regulatory networks in acne, providing a more comprehensive understanding and novel therapeutic targets of acne.

Sepsis is the major cause of mortality in the intensive care unit. The aim of this study was to identify the key prognostic biomarkers of abnormal expression and immune infiltration in sepsis. In this study, a total of 36 differentially expressed genes were identified to be mainly involved in a number of immune-related Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. The hub genes ( and ) were significantly related to the prognosis of sepsis patients. The immune infiltration analysis indicated a significant difference in the relative cell content of naive B cells, follicular Th cells, activated NK cells, eosinophils, neutrophils and monocytes between sepsis and normal controls. Weighted gene co-expression network analysis and a de-convolution algorithm that quantifies the cellular composition of immune cells were used to analyse the sepsis expression data from the Gene Expression Omnibus database and to identify modules related to differential immune cells. is the key immune-related gene that may be involved in sepsis. Gene set enrichment analysis revealed that is involved in the processes of T cell selection, B cell-mediated immunity, NK cell activation and pathways of T cells, B cells and NK cells. Therefore, may play a key role in the biological and immunological processes of sepsis.

Systemic juvenile idiopathic arthritis (SJIA) is a severe childhood-onset inflammatory disease characterized by arthritis accompanied by systemic auto-inflammation and extra-articular symptoms. While recent advances have unraveled a range of risk factors, the pathomechanisms involved in SJIA and potential prognostic markers for treatment success remain partly unknown. In this study, we included 70 active SJIA and 55 healthy control patients from the National Center for Biotechnology Information to analyze for differentially expressed genes (DEGs) using R. Functional enrichment analysis, protein-protein interaction (PPI), and gene module construction were performed for DEGs and hub gene set. We additionally examined immune system cell composition with CIBERSORT and predicted prognostic markers and potential treatment drugs for SJIA. In total, 94 upregulated and 24 downregulated DEGs were identified. Two specific modules of interest and eight hub genes (ARG1, DEFA4, HP, MMP8, MMP9, MPO, OLFM4, PGLYRP1) were screened out. Functional enrichment analysis suggested that complex neutrophil-related functions play a decisive role in the disease pathogenesis. CIBERSORT indicated neutrophils, M0 macrophages, CD8+ T cells, and naïve B cells to be relevant drivers of disease progression. Additionally, we identified TPM2 and GZMB as potential prognostic markers for treatment response to canakinumab. Moreover, sulindac sulfide, (-)-catechin, and phenanthridinone were identified as promising treatment agents. This study provides a new insight into molecular and cellular pathogenesis of active SJIA and highlights potential targets for further research.

BACKGROUND:Ankylosing spondylitis (AS) is a progressive rheumatic disease and studies reveal that the immune system is critical for the pathogenesis of AS. In the present study, various bioinformatics analysis methods were comprehensively applied, designed to identify potential key genes and inflammation states of AS. METHODS:The transcriptome profiles of GSE25101 and GSE73754 obtained from the Gene Expression Omnibus (GEO) database were merged for subsequent analyses. The differentially expressed genes (DEGs) were identified using the Bioconductor package Limma and threshold values. Functional enrichment and pathway enrichment analyses were performed using the clusterProfiler package and Gene Set Enrichment Analysis (GSEA). Next, protein-protein interaction (PPI) network of the identified DEGs was constructed by the online database, the Search Tool for the Retrieval of Interacting Genes (STRING), visualization and analysis were performed through Cytoscape software. Subsequently, we applied CIBERSORT algorithm to identify subpopulation proportions of immune cells in peripheral blood samples. Finally, we validated the hub genes with the GSE18781 dataset. Samples were collected from patients to validate gene and protein expression using qRT-PCR and ELISA. RESULTS:A total of 334 DEGs were identified, including 182 upregulated and 152 downregulated DEGs, between AS patients and normal human controls, which were primarily involved in immune response, autophagy, and natural killer cell-mediated cytotoxicity. The most prominent module and candidate biomarkers were identified from the PPI network. Biomarkers were selected for validation and their expressions were significantly decreased in peripheral blood samples which was consistent with transcriptome sequencing results. Nine genes with AUC > 0.70 were considered to be AS hub genes for ROC curve analysis, including GZMA, GZMK, PRF1, GNLY, NKG7, KLRB1, KLRD1, IL2RB and CD247. Furthermore, CIBERSORT results suggest that AS contained a higher proportion of CD8+ T cells, naive CD4+ T cells, neutrophils, and lower levels of gamma delta T cells compared with the normal controls. CONCLUSION:In this study, we identified DEGs combined with their closely related biological functions and propose that granule-associated proteins and immune infiltration maybe involved in the progression of ankylosing spondylitis. These validated hub genes may provide new perspectives for understanding the molecular mechanisms of ankylosing spondylitis.

Chronic Chagas cardiomyopathy (CCC) is an acquired inflammatory cardiomyopathy triggered by the protozoan Trypanosoma cruzi infection. Although microvascular and neurogenic dysfunction and inflammation with persistent parasite presence in the heart may play a major pathogenetic role, little is known about the overall picture of gene co-expression regulating CCC. In this study, we aimed to explore the key biological pathways, hub genes and the landscope of infiltrating immune cells associated with inflammation in chronic Chagas cardiomyopathy. A weighted gene co-expression network analysis (WGCNA) was conducted based on the gene expression profiles from patients with and without chronic Chagas cardiomyopathy (GSE84796). Twelve coexpression modules were identified from the top 25% variant genes. Among them, the turquoise and black module were significantly positively correlated with CCC, which were highly enriched in Th1 and Th2 cell differentiation, the Cytokine-cytokine receptor interaction,NF-kappa B signaling pathway and T cell receptor signaling pathway. In addition, four genes (TBX21, ZAP70,IL2RB and CD69) were selected as candidate hub genes. Gene expression for hub genes were higher in CCC tissues compared to tissues from healthy controls. Additionally, gene set enrichment analysis (GSEA) analysis showed that high expressions of these hub genes were significantly correlated with interferon α response and interferon γ response. The microarray dataset GSE41089 further confirmed that although CD69 was not detected, the expression of TBX21, IL2RB and ZAP70 was also significantly up-regulated in the CCC mice compared to controls. We further studied the immune cells infiltration in CCC patients with CIBERSORT. The fraction of Mast cells activated,T cells CD8 and T cells gamma delta were significantly increased in CCC patients compared with control. Our research provides a more effective understanding of the pathogenesis of CCC and could help in future strategies for new diagnostic and therapeutic approaches for CCC patients.

This study aimed to explore the molecular mechanisms related to immune and hub genes related to pulmonary arterial hypertension (PAH). The differentially expressed genes (DEGs) of GSE15197 were identified as filters with adjusted P value <0.05, and |Log2 fold change|> 1. Biofunctional and pathway enrichment annotation of DEGs indicated that immunity and inflammation may play an important role in the molecular mechanism of PAH. The CIBERSORT algorithm further analyzed the immune cell infiltration characteristics of the PAH and control samples. Subsequently, 16 hub genes were identified from DEGs using the least absolute shrinkage and selection operator (LASSO) algorithm. An immune related gene CX3CR1 was further selected from the intersection results of the 16 hub genes and the top 20 genes with the most adjacent nodes in the protein-protein interaction (PPI) network. GSE113439, GSE48149, and GSE33463 datasets were used to validate and proved CX3CR1 with a remarkable score of AUC to distinguish PAH samples caused by various reasons from the control group.

Idiopathic pulmonary arterial hypertension (IPAH) is a rare but severe lung disorder, which may lead to heart failure and early mortality. However, little is known about the etiology of IPAH. Thus, the present study aimed to establish the differentially expressed genes (DEGs) between IPAH and normal tissues, which may serve as potential prognostic markers in IPAH. Furthermore, we utilized a versatile computational method, CIBERSORT to identify immune cell infiltration characteristics in IPAH. The GSE117261 and GSE48149 datasets were obtained from the Gene Expression Omnibus database. The GSE117261 dataset was adopted to screen DEGs between IPAH and the control groups with the criterion of |log2 fold change| ≥ 1, adjusted < 0.05, and to further explore their potential biological functions via Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes Pathway analysis, and Gene Set Enrichment Analysis. Moreover, the support vector machine (SVM)-recursive feature elimination and the least absolute shrinkage and selection operator regression model were performed jointly to identify the best potential biomarkers. Then we built a regression model based on these selected variables. The GSE48149 dataset was used as a validation cohort to appraise the diagnostic efficacy of the SVM classifier by receiver operating characteristic (ROC) analysis. Finally, immune infiltration was explored by CIBERSORT in IPAH. We further analyzed the correlation between potential biomarkers and immune cells. In total, 75 DEGs were identified; 40 were downregulated, and 35 genes were upregulated. Functional enrichment analysis found a significantly enrichment in heme binding, inflammation, chemokines, cytokine activity, and abnormal glycometabolism. , and were identified as the best potential biomarkers with an area under the ROC curve (AUC) of 1 (95%CI = 0.937-1.000, specificity = 100%, sensitivity = 100%) in the discovery cohort and 1(95%CI = 0.805-1.000, specificity = 100%, sensitivity = 100%) in the validation cohort. Moreover, immune infiltration analysis by CIBERSORT showed a higher level of CD8+ T cells, resting memory CD4+ T cells, gamma delta T cells, M1 macrophages, resting mast cells, as well as a lower level of naïve CD4+ T cells, monocytes, M0 macrophages, activated mast cells, and neutrophils in IPAH compared with the control group. In addition, , and were correlated with immune cells. , and were identified as potential biomarkers to discriminate IPAH from the control. There was an obvious difference in immune infiltration between patient with IPAH and normal groups.

Endometriosis (EM) is a chronic neuroinflammatory disorder that is associated with pain and infertility that affects ∼10% of reproductive-age women. The pathophysiology and etiology of EM remain poorly understood, and diagnostic delays are common. Exploration of the underlying molecular mechanism, as well as novel diagnostic biomarkers and therapeutic targets, is urgently needed. Inflammation is known to play a key role in the development of lesions, which are a defining feature of the disorder. In our research, the CIBERSORT and WGCNA algorithms were used to establish a weighted gene co-expression network and to identify macrophage-related hub genes using data downloaded from the GEO database (GSE11691, 7305). The analysis identified 1,157 differentially expressed genes (DEGs) in EM lesions, of which five were identified as being related to M2 macrophages and were validated as differentially expressed by qRT-PCR and immunohistochemistry (IHC). Of these putative novel biomarker genes, bridging integrator 2 (BIN2), chemokine receptor 5 (CCR5), and macrophage mannose receptor 1 (MRC1) were upregulated, while spleen tyrosine kinase (SYK) and metalloproteinase 12 (ADAM12) were downregulated in ectopic endometria vs. normal endometria. Meanwhile, 23 potentially therapeutic small molecules for EM were obtained from the cMAP database, among which topiramate, isoflupredone, adiphenine, dexverapamil, MS-275, and celastrol were the top six molecules with the highest absolute enrichment values. This is our first attempt to use the CIBERSORT and WGCNA algorithms for the identification of novel Mϕ2 macrophage-related biomarkers of EM. Our findings provide novel insights into the impact of immune cells on the etiology of EM; nevertheless, further investigation of these key genes and therapeutic drugs is needed to validate their effects on EM.

AIMS:To explore immune cell infiltration characteristics of, and hub genes associated with, hypertrophic cardiomyopathy (HCM). MATERIALS AND METHODS:The GSE130036 dataset was downloaded and the differentially expressed genes (DEGs) were identified. The DEGs were analyzed via the CIBERSORT algorithm to understand the composition of 22 immune cell types between the HCM and normal myocardial tissue specimens. Weighted gene co-expression network analysis (WGCNA) was performed to segregate the DEGs into several modules and explore correlation between the key modules and specific immune cells enriched in the myocardial tissues of HCM patients. The biofunctional and disease enrichment of the genes among the modules was explored, and hub genes serving as potential biomarkers of HCM were identified. These genes were validated by GSE36961 dataset, and the discrimination ability was assessed by receiver operating characteristic curve analysis. KEY FINDINGS:CIBERSORT analysis showed that neutrophils and B-cells (naive and memory B-cells) were highly abundant in HCM samples, while macrophages (M0, M1, M2) were highly abundant in normal samples. WGCNA analysis of the DEGs yielded seven modules, and the gray and yellow modules were strongly associated with neutrophils and B-cells, and with macrophages, respectively. Yellow module genes were mainly functional in immune and inflammation processes. Gray module genes were mainly functional in the transportation of intercellular substances. SLITRK4 and CD163 showed a notably high area under the curve values in both datasets and may serve as potential biomarkers for HCM. SIGNIFICANCE:SLITRK4 and CD163 may be promising Diagnostic Biomarkers of Hypertrophic Cardiomyopathy.

In the context of transplantation with the use of immunosuppressive drugs, BK virus infection has become the main cause of BK virus nephropathy(BKVN) in renal transplant recipients(KTRs). More importantly, BKVN may cause further allograft dysfunction and loss. However, the role of the immune microenvironment in the pathogenesis of BKVN remains unknown. Therefore, we collected microarray data of KTRs to elucidate the immune characteristics of BKVN. Via the CIBERSORT, we found that BKVN had relatively more activated memory CD4 T cells. Immunostaining showed that CD4+ and CD8+cells were significantly different between BKVN and stable allografts(STAs). In addition, the expression of immune-related genes(antigen presentation, cytotoxicity, and inflammation) was significantly higher in BKVN than in STAs. The results of gene set enrichment analysis(GSEA) and single-sample GSEA(ssGSEA) indicated that immune cell-,cytokine-,chemokine-, and inflammation-related pathways were significantly activated in BKVN, while metabolism- and renal development-related pathways were significantly downregulated in BKVN. In addition, the immune microenvironments of the peripheral blood in patients with BK viremia(BKV) or transplant kidney biopsy(TKB) with BKVN may be different. Overall, the immune microenvironment may play important roles in the occurrence and development of BKVN and provide a theoretical basis for preventing the occurrence of BKVN and finding novel treatments.

Freedivers hold their breath while diving, causing blood oxygen levels to decrease (hypoxia) while carbon dioxide increases (hypercapnia). Whereas blood gas changes are presumably involved in the progression of respiratory diseases, less is known about their effect on healthy individuals. Here we have used gene expression profiling to analyze elite athletes' immune and inflammatory responses to freediving. Blood was collected before and 1 and 3 h after a series of maximal dynamic and static freediving apneas in a pool, and peripheral blood gene expression was mapped on genome-wide microarrays. Fractions of phenotypically distinct immune cells were computed by deconvolution of the gene expression data using Cibersort software. Changes in gene activity and associated biological pathways were determined using R and GeneGo software. The results indicated a temporary increase of neutrophil granulocytes, and a decrease of cytotoxic lymphocytes; i.e., CD8+ T cells and resting NK cells. Biological pathway associations indicated possible protective reactions: genes involved in anti-inflammatory responses to proresolving lipid mediators were upregulated, whereas central factors involved in granule-mediated lymphocyte cytotoxicity were downregulated. While it remains unresolved whether freediving alters the immune system's defensive function, these results provide new insight into leukocyte responses and the protection of homeostasis in healthy athletes.

The pandemic of the coronavirus disease 2019 (COVID-19) has posed huge threats to healthcare systems and the global economy. However, the host response towards COVID-19 on the molecular and cellular levels still lacks full understanding and effective therapies are in urgent need. Here, we integrate three datasets, GSE152641, GSE161777, and GSE157103. Compared to healthy people, 314 differentially expressed genes were identified, which were mostly involved in neutrophil degranulation and cell division. The protein-protein network was established and two significant subsets were filtered by MCODE: ssGSEA and CIBERSORT, which comprehensively revealed the alternation of immune cell abundance. Weighted gene coexpression network analysis (WGCNA) as well as GO and KEGG analyses unveiled the role of neutrophils and T cells during the progress of the disease. Based on the hospital-free days after 45 days of follow-up and statistical methods such as nonnegative matrix factorization (NMF), submap, and linear correlation analysis, 31 genes were regarded as the signature of the peripheral blood of COVID-19. Various immune cells were identified to be related to the prognosis of the patients. Drugs were predicted for the genes in the signature by DGIdb. Overall, our study comprehensively revealed the relationship between the inflammatory response and the disease course, which provided strategies for the treatment of COVID-19.

OBJECTIVE:To investigate the potential role of placenta inflammation in gestational diabetes mellitus (GDM) and construct a model for the diagnosis of GDM. METHODS:In this study, transcriptome-wide profiling datasets, GSE70493 and GSE128381 were downloaded from Gene Expression Omnibus (GEO) database. Significant immune-related genes were identified separately to be the biomarkers for the diagnosis of GDM by using random forest model (RF), support vector machine model (SVM), and generalized linear model (GLM). RESULTS:RF was the best model and was used to select the four key immune-related genes (FABP4, DKK1, CXCL10, and IL1RL1) to diagnose GDM. A nomogram model was constructed to predict GDM based on the four key immune-related genes by using "rms" package. The relative proportion of 22 immune cell types were calculated by using CIBERSORT algorithm. Higher M1 macrophage ratio and lower M2 macrophage ratio in GDM placenta compared to normal patients were observed. CONCLUSIONS:This study provides clues that inflammation was correlated with GDM and suggests inflammation may be the cause and also the potential targets of GDM.

Rheumatoid arthritis (RA) is an autoimmune disease characterized by erosive arthritis, which has not been thoroughly cured yet, and standardized treatment is helpful for alleviating clinical symptoms. Here, various bioinformatics analysis tools were comprehensively utilized, aiming to identify critical biomarkers and possible pathogenesis of RA. Three gene expression datasets profiled by microarray were obtained from GEO database. Dataset GSE55235 and GSE55457 were merged for subsequent analyses. We identified differentially expressed genes (DEGs) in RStudio with limma package, performing functional enrichment analysis based on GSEA software and clusterProfiler package. Next, protein-protein interaction (PPI) network was set up through STRING database and Cytoscape. Moreover, CIBERSORT website was used to assess the inflammatory state of RA. Finally, we validated the candidate hub genes with dataset GSE77298. As a result, we identified 106 DEGs (72 upregulated and 34 downregulated genes). Through GO, KEGG, and GSEA analysis, we found that DEGs were mainly involved in immune response and inflammatory signaling pathway. With the help of Cytoscape software and MCODE plug-in, the most prominent subnetwork was screened out, containing 14 genes and 45 edges. For ROC curve analysis, eight genes with AUC >0.80 were considered as hub genes of RA. In conclusion, compared with healthy controls, the DEGs and their closely related biological functions were analyzed, and we held that chemokines and immune cells infiltration promote the progression of rheumatoid arthritis. Targeting the eight biomarkers we identified may be useful for the diagnosis and treatment of rheumatoid arthritis.

Ulcerative colitis (UC) is a common disease with great variability in severity, with a high recurrence rate and heavy disease burden. In recent years, the different biological functions of competing endogenous RNA (ceRNA) networks of long noncoding RNAs (lncRNAs) and microRNAs (miRs) have aroused wide concerns, the ceRNA network of ulcerative colitis (UC) may have potential research value, and these expressed noncoding RNAs may be involved in the molecular basis of inflammation recurrence and progression. This study analyzed 490 colon samples associated with UC from 4 gene expression microarrays from the GEO database and identified gene modules by weighted correlation network analysis (WGCNA). CIBERSORT detected tissue-infiltrating leukocyte profiling by deconvolution of microarray data. LncBase and multiMIR were used to identify lncRNA-miRNA-mRNA interaction. We constructed a ceRNA network which includes 4 lncRNAs (SH3BP5-AS1, MIR4435-2HG, ENTPD1-AS1, and AC007750.1), 5 miRNAs (miR-141-3p, miR-191-5p, miR-192-5p, miR-194-5p, and miR196-5p), and 52 mRNAs. Those genes are involved in interleukin family signals, neutrophil degranulation, adaptive immunity, and cell adhesion pathways. lncRNA MIR4435-2HG is a variable in the decision tree for moderate-to-severe UC diagnostic prediction. Our work identifies potential regulated inflammation-related lncRNA-miRNA-mRNA regulatory axes. The regulatory axes are dysregulated during the deterioration of UC, suggesting that it is a risk factor for UC progression.