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    Identification of Senescence-Related Subtypes, the Development of a Prognosis Model, and Characterization of Immune Infiltration and Gut Microbiota in Colorectal Cancer. Frontiers in medicine Cellular senescence is associated with tumorigenesis, and the subtype and prognostic signatures of senescence-related genes (SRGs) in the tumor microenvironment (TME) and gut microbiota have not been fully determined. Analysis of 91 SRGs obtained from the GSEA and MSigDB, and mRNA sequencing of genes in the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases enabled the identification of two distinct molecular types of colorectal cancer (CRC). Patient samples were clustered into two subtypes, with Kaplan-Meier survival analyses showing significant differences in patient survival between the two subtypes. Cluster C2 was associated with patient clinicopathological features, high immune score, high abundance of immune infiltrating cells and somewhat high abundance of bacteria. A risk model based on eight SRGs showed that a low risk score was characterized by inhibition of immune activity and was indicative of better prognosis in patients with CRC. In combination with clinical characteristics, risk score was found to be an independent prognostic predictor of survival in patients with CRC. In conclusion, the present study showed that senescence-related subtypes and a signature consisting of eight SRGs were associated with CRC patient prognosis, as well as with immune cell infiltration and gut microbiota. These findings may enable better prediction of CRC patient prognosis and facilitate individualized treatments. 10.3389/fmed.2022.916565
    Identification and Validation of Aging-Related Genes in Alzheimer's Disease. Frontiers in neuroscience Aging is recognized as the key risk factor for Alzheimer's disease (AD). This study aimed to identify and verify potential aging-related genes associated with AD using bioinformatics analysis. Aging-related differential expression genes (ARDEGs) were determined by the intersection of limma test, weighted correlation network analysis (WGCNA), and 1153 aging and senescence-associated genes. Potential biological functions and pathways of ARDEGs were determined by GO, KEGG, GSEA, and GSVA. Then, LASSO algorithm was used to identify the hub genes and the diagnostic ability of the five ARDEGs in discriminating AD from the healthy control samples. Further, the correlation between hub ARDEGs and clinical characteristics was explored. Finally, the expression level of the five ARDEGs was validated using other four GEO datasets and blood samples of patients with AD and healthy individuals. Five ARDEGs (GFAP, PDGFRB, PLOD1, MAP4K4, and NFKBIA) were obtained. For biological function analysis, aging, cellular senescence, and Ras protein signal transduction regulation were enriched. Diagnostic ability of the five ARDEGs in discriminating AD from the control samples demonstrated a favorable diagnostic value. Eventually, quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR) validation test revealed that compared with healthy controls, the mRNA expression level of PDGFRB, PLOD1, MAP4K4, and NFKBIA were elevated in AD patients. In conclusion, this study identified four ARDEGs (PDGFRB, PLOD1, MAP4K4, and NFKBIA) associated with AD. They provide an insight into potential novel biomarkers for diagnosing AD and monitoring progression. 10.3389/fnins.2022.905722
    Identification and Validation of CDKN1A and HDAC1 as Senescence-Related Hub Genes in Chronic Obstructive Pulmonary Disease. International journal of chronic obstructive pulmonary disease Purpose:Cellular senescence participates in the occurrence and development of chronic obstructive pulmonary disease (COPD). This study aimed to identify senescence-related hub genes and explore effective diagnostic markers and therapeutic targets for COPD. Methods:The microarray data from the GSE38974 dataset was downloaded from the Gene Expression Omnibus (GEO) database. The overlapping genes between genes from the GSE38974 dataset and CellAge database were considered differentially expressed senescence-related genes (DESRGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using R software. Protein-protein interaction (PPI), miRNA-mRNA network, and competitive endogenous RNA (ceRNA) network were constructed and visualized by Cytoscape software. GSE100281 and GSE103174 datasets were employed to validate the expression and diagnostic value of hub genes. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to measure the mRNA levels of hub genes in peripheral blood mononuclear cells (PBMCs) from COPD and control samples. Results:A total of 23 DESRGs were identified between COPD samples and healthy controls. Enrichment analysis revealed that DESRGs were mainly related to apoptosis and senescence. Moreover, four hub genes and two key clusters were acquired by Cytohubba and MCODE plugin, respectively. CDKN1A and HDAC1 were verified as final hub genes based on GSE100281 and GSE103174 datasets validation. The mRNA expression level of CDKN1A was negatively related to forced expiratory volume in 1 second/forced vital capacity (FEV1/FVC), and HDAC1 expression had the opposite correlation. Finally, an HDAC1-based ceRNA network, including 6 miRNAs and 11 lncRNAs, was constructed. Conclusion:We identified two senescence-related hub genes, CDKN1A and HDAC1, which may be effective biomarkers for COPD diagnosis and treatment. An HDAC1-related ceRNA network was constructed to clarify the role of senescence in COPD pathogenesis. 10.2147/COPD.S374684
    Establishment of three heterogeneous subtypes and a risk model of low-grade gliomas based on cell senescence-related genes. Frontiers in immunology Background:Cellular senescence is a key element in the occurrence and progression of a variety of tumors. As a result, cellular senescence-related markers can be categorized based on the prognosis status of patients. Due to the heterogeneity and the complexity of the tumor microenvironment (TME), the long-term effectiveness of low-grade glioma (LGG) treatment remains a clinical challenge. Consequently, developing and refining effective treatment approaches to aid with LGG management is critical. Methods:Based on the expressions of cell senescence-related genes (CSRGs) acquired from the cellAge database, consensus clustering was utilized to identify stable molecular subtypes. Clinical features, immune infiltration, route modifications, and genetic changes of various subtypes were also assessed. Following that, the least absolute shrinkage and selection operator (LASSO) regression and univariate Cox regression analysis were used for developing the cell senescence-related risk score (CSRS) model. Finally, a correlation study of the CSRS model with molecular, immunological, and immunotherapy parameters was performed. Results:C1, C2, and C3, are the three senescence-related subtypes that were identified. Patients belonging to the C1 subtype had poor prognoses and a substantial proportion of them was in the grade G3. The differentially expressed genes (DEGs) among the three subtypes were used to develop the CSRS model. In both the training and independent validation cohort, the model had a high area under the receiver operating characteristic (ROC) curve in predicting the overall survival (OS) of patients. As a result, this model can predict clinical features and responses to immunotherapy in a variety of patients and it is a potential independent prognostic factor for LGG. Conclusion:This research discovered three LGG subtypes related to cell senescence and created a CSRS model for six genes. Cell senescence was highly associated with unfavorable prognosis in LGG. The CSRS model can be used to predict the prognosis of patients and identify patients who would benefit from immunotherapy. 10.3389/fimmu.2022.982033
    Identification and validation of a novel cellular senescence-related lncRNA prognostic signature for predicting immunotherapy response in stomach adenocarcinoma. Frontiers in genetics Cellular senescence is a novel hallmark of cancer associated with patient outcomes and tumor immunotherapy. However, the value of cellular senescence-related long non-coding RNAs (lncRNAs) in predicting prognosis and immunotherapy response for stomach adenocarcinoma (STAD) patients needs further investigation. The transcriptome and corresponding clinical information of STAD and cellular senescence-related genes were, respectively, downloaded from the Cancer Genome Atlas (TCGA) and CellAge databases. Differential expression analysis and coexpression analysis were performed to obtain cellular senescence-related lncRNAs. Univariate regression analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were conducted to establish the cellular senescence-related lncRNA prognostic signature (CSLPS). Next, the survival curve, ROC curve, and nomogram were developed to assess the capacity of predictive models. Moreover, principal component analysis (PCA), gene set enrichment analysis (GSEA), tumor microenvironment (TME), tumor mutation burden (TMB), microsatellite instability (MSI), and tumor immune dysfunction and exclusion (TIDE) score analysis were performed between high- and low-risk groups. A novel CSLPS involving fifteen lncRNAs (REPIN1-AS1, AL355574.1, AC104695.3, AL033527.2, AC083902.1, TYMSOS, LINC00460, AC005165.1, AL136115.1, AC007405.2, AL391152.1, SCAT1, AC129507.1, AL121748.1, and ADAMTS9-AS1) was developed. According to the nomogram, the risk model based on the CSLPS was an independent prognostic factor and could predict 1-, 3-, and 5-year overall survival for STAD patients. GSEA suggested that the high-risk group was mainly associated with Toll-like receptor, JAK/STAT, NOD-like receptor, and chemokine signaling pathways. Further analysis revealed that STAD patients in the low-risk group with better clinical outcomes had a higher TMB, higher proportion of high microsatellite instability (MSI-H), better immune infiltration, and lower TIDE scores. A fifteen-CSlncRNA prognostic signature could predict survival outcomes, and patients in the low-risk group may be more sensitive to immunotherapy. 10.3389/fgene.2022.935056
    Subtype Classification and Prognosis Signature Construction of Osteosarcoma Based on Cellular Senescence-Related Genes. Journal of oncology Background:Cellular senescence (CS) is an alternative procedure that replaces or reinforces inadequate apoptotic responses and is used as an influencing factor for a variety of cancers. The value of CS gene in evaluating the immunotherapy response and clinical outcome of osteosarcoma (OS) has not been reported, and an accurate risk model based on CS gene has not been developed for OS patients. Methods:279 CS genes were obtained from CellAge. Univariate Cox regression analysis was used to screen the CS gene which was significantly related to the prognosis of OS samples in TARGET data set. The prognosis, clinicopathological features, immune infiltration, gene expression at immune checkpoints, tumor immune dysfunction and exclusion (TIDE) score, and chemotherapy resistance of OS were analyzed among clusters. Least absolute shrinkage and selection operator (Lasso) Cox regression analysis to build cellular senescence-related gene signature (CSRS). Univariate and multivariate Cox regression analysis of CSRS and clinical parameters were carried out, and the parameters with independent prognostic value were used to construct nomogram. Results:Based on 30 CS genes related to OS prognosis, OS samples were divided into three clusters: C1, C2, and C3. C3 showed the lowest survival rate and metastasis rate and the highest immune score and stromal score and was more likely to respond to immune checkpoint blockade (ICB) treatment. A CSRS scoring system including four CS genes (MYC, DLX2, EPHA3, and LIMK1) was constructed, which could distinguish the survival outcome, tumor microenvironment (TME) status, and ICB treatment response of patients with different CSRS score. Nomogram constructed by CSRS score and metastatic has a high prognostic value for OS. Conclusions:Our study identified a molecular classification determined by CS-related genes and developed a new CSRS that has potential value in OS immunotherapy response and clinical outcome prediction. 10.1155/2022/4421952