Dataset Information

Identifying the pattern of immune related cells and genes in the peripheral blood of ischemic stroke.

ABSTRACT: BACKGROUND:Ischemic stroke (IS) is the second leading cause of death worldwide which is a serious hazard to human health. Evidence suggests that the immune system plays a key role in the pathophysiology of IS. However, the precisely immune related mechanisms were still not been systematically understood. METHODS:In this study, we aim to identify the immune related modules and genes that might play vital role in the occurrence and development of IS by using the weighted gene co-expression network analysis (WGCNA). Meanwhile, we applied a kind of deconvolution algorithm to reveal the proportions of 22 subsets of immune cells in the blood samples. RESULTS:There were total 128 IS patients and 67 healthy control samples in the three Gene Expression Omnibus (GEO) datasets. Under the screening criteria, 1082 DEGs (894 up-regulated and 188 down-regulated) were chosen for further analysis. A total of 11 clinically significant modules were identified, from which immune-related hub modules and hub genes were further explored. Finally, 16 genes were selected as real hub genes for further validation analysis. Furthermore, these CIBERSORT results suggest that detailed analysis of the immune subtype distribution pattern has the potential to enhance clinical prediction and to identify candidates for immunotherapy. More specifically, we identified that neutrophil emerge as a promising target for IS therapies. CONCLUSIONS:In the present study, we investigated the immune related gene expression modules, in which the SLAMF1, IL7R and NCF4 may be novel therapeutic targets to promote functional and histological recovery after ischemic stroke. Furthermore, these hub genes and neutrophils may become important biological targets in the drug screening and drug designing.

SUBMITTER: Li Z

PROVIDER: S-EPMC7398186 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Identifying the pattern of immune related cells and genes in the peripheral blood of ischemic stroke.

Li Zijian Z Cui Yueran Y Feng Juan J Guo Yanxia Y

Journal of translational medicine 20200803 1

<h4>Background</h4>Ischemic stroke (IS) is the second leading cause of death worldwide which is a serious hazard to human health. Evidence suggests that the immune system plays a key role in the pathophysiology of IS. However, the precisely immune related mechanisms were still not been systematically understood.<h4>Methods</h4>In this study, we aim to identify the immune related modules and genes that might play vital role in the occurrence and development of IS by using the weighted gene co-exp ...[more]

PMID: 32746852

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Project description:Background Immune infiltration plays an important role in the course of ischemic stroke (IS) progression. Cuproptosis is a newly discovered form of programmed cell death. To date, no studies on the mechanisms by which cuproptosis-related genes regulate immune infiltration in IS have been reported. Methods IS-related microarray datasets were retrieved from the Gene Expression Omnibus (GEO) database and standardized. Immune infiltration was extracted and quantified based on the processed gene expression matrix. The differences between the IS group and the normal group as well as the correlation between the infiltrating immune cells and their functions were analyzed. The cuproptosis-related DEGs most related to immunity were screened out, and the risk model was constructed. Finally, Gene Ontology (GO) function, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and drug target were performed using the Enrichr website database. miRNAs were predicted using FunRich software. Finally, cuproptosis-related differentially expressed genes (DEGs) in IS samples were typed, and Gene Set Variation Analysis (GSVA) was used to analyze the differences in biological functions among the different types. Results Seven Cuproptosis-related DEGs were obtained by merging the GSE16561 and GSE37587 datasets. Correlation analysis of the immune cells showed that NLRP3, NFE2L2, ATP7A, LIPT1, GLS, and MTF1 were significantly correlated with immune cells. Subsequently, these six genes were included in the risk study, and the risk prediction model was constructed to calculate the total score to analyze the risk probability of the IS group. KEGG analysis showed that the genes were mainly enriched in the following two pathways: D-glutamine and D-glutamate metabolism; and lipids and atherosclerosis. Drug target prediction found that DMBA CTD 00007046 and Lithocholate TTD 00009000 were predicted to have potential therapeutic effects of candidate molecules. GSVA showed that the TGF-β signaling pathway and autophagy regulation pathways were upregulated in the subgroup with high expression of cuproptosis-related DEGs. Conclusions NLRP3, NFE2L2, ATP7A, LIPT1, GLS and MTF1 may serve as predictors of cuproptosis and play an important role in the pathogenesis of immune infiltration in IS.

Project description:BackgroundAnkylosing 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.MethodsThe 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.ResultsA 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.ConclusionIn 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.

Dataset Information

Identifying the pattern of immune related cells and genes in the peripheral blood of ischemic stroke.

Publications

Identifying the pattern of immune related cells and genes in the peripheral blood of ischemic stroke.

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