Bioinformatics prediction of function of T-cell exhaustion related genes in ischemic stroke
Abstract Ischemic stroke (IS) is a multifactorial disease caused by the interaction of a variety of environmental and genetic factors, which can lead to severe disability and heavy social burden. This study aimed to find potential biomarkers related to T cell exhaustion (TEX) in IS. Based on the GSE...
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Nature Portfolio
2025-05-01
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| Online Access: | https://doi.org/10.1038/s41598-025-03724-y |
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| author | Yajun Gao Ruyu Bai Bo Gao Ma Li |
| author_facet | Yajun Gao Ruyu Bai Bo Gao Ma Li |
| author_sort | Yajun Gao |
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| description | Abstract Ischemic stroke (IS) is a multifactorial disease caused by the interaction of a variety of environmental and genetic factors, which can lead to severe disability and heavy social burden. This study aimed to find potential biomarkers related to T cell exhaustion (TEX) in IS. Based on the GSE16561 dataset, differentially expressed genes (DEGs) were screened from IS and control groups, and their enriched biological pathways were explored. The TEX enrichment score for each sample was calculated using the GSEA algorithm, and the gene modules with the highest correlation with the TEX score were screened by WGCNA. Then, two machine learning algorithms were used to screen the key genes and test the correlation between the key genes and the level of immune cell infiltration. Potential drugs or molecular compounds that interact with key genes were predicted by searching DGIdb, and the drug-gene interaction network was visualized by Cytoscape software. Using GSE16561 dataset, we performed differential expression analysis and identified 482 DEGs. By weighted gene co-expression network analysis (WGCNA) and machine learning algorithms, we identified five key genes: CD163, LAMP2, PICALM, RGS2 and PIN1. Functional enrichment analysis revealed that these genes were involved in immune response and cellular processes, which were closely related to the level of immune cell infiltration. In addition, potential drug interactions were predicted using the drug-Gene Interaction database, providing avenues for future therapeutic strategies. This study enhances the understanding of TEX-related biomarkers in ischemic stroke and provides insights into the development of novel interventions aimed at improving patient outcomes. |
| format | Article |
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| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-b4740f896e5148d5baa32c9d1eba53002025-08-20T03:16:40ZengNature PortfolioScientific Reports2045-23222025-05-0115111010.1038/s41598-025-03724-yBioinformatics prediction of function of T-cell exhaustion related genes in ischemic strokeYajun Gao0Ruyu Bai1Bo Gao2Ma Li3Department of Neurology, Yan’an People’s HospitalDepartment of Neurology, Yan’an People’s HospitalDepartment of Neurology, Yan’an University Affiliated HospitalDepartment of Neurology, Yan’an University Affiliated HospitalAbstract Ischemic stroke (IS) is a multifactorial disease caused by the interaction of a variety of environmental and genetic factors, which can lead to severe disability and heavy social burden. This study aimed to find potential biomarkers related to T cell exhaustion (TEX) in IS. Based on the GSE16561 dataset, differentially expressed genes (DEGs) were screened from IS and control groups, and their enriched biological pathways were explored. The TEX enrichment score for each sample was calculated using the GSEA algorithm, and the gene modules with the highest correlation with the TEX score were screened by WGCNA. Then, two machine learning algorithms were used to screen the key genes and test the correlation between the key genes and the level of immune cell infiltration. Potential drugs or molecular compounds that interact with key genes were predicted by searching DGIdb, and the drug-gene interaction network was visualized by Cytoscape software. Using GSE16561 dataset, we performed differential expression analysis and identified 482 DEGs. By weighted gene co-expression network analysis (WGCNA) and machine learning algorithms, we identified five key genes: CD163, LAMP2, PICALM, RGS2 and PIN1. Functional enrichment analysis revealed that these genes were involved in immune response and cellular processes, which were closely related to the level of immune cell infiltration. In addition, potential drug interactions were predicted using the drug-Gene Interaction database, providing avenues for future therapeutic strategies. This study enhances the understanding of TEX-related biomarkers in ischemic stroke and provides insights into the development of novel interventions aimed at improving patient outcomes.https://doi.org/10.1038/s41598-025-03724-yIschemic strokeT cell exhaustionBiomarkersMachine learning |
| spellingShingle | Yajun Gao Ruyu Bai Bo Gao Ma Li Bioinformatics prediction of function of T-cell exhaustion related genes in ischemic stroke Scientific Reports Ischemic stroke T cell exhaustion Biomarkers Machine learning |
| title | Bioinformatics prediction of function of T-cell exhaustion related genes in ischemic stroke |
| title_full | Bioinformatics prediction of function of T-cell exhaustion related genes in ischemic stroke |
| title_fullStr | Bioinformatics prediction of function of T-cell exhaustion related genes in ischemic stroke |
| title_full_unstemmed | Bioinformatics prediction of function of T-cell exhaustion related genes in ischemic stroke |
| title_short | Bioinformatics prediction of function of T-cell exhaustion related genes in ischemic stroke |
| title_sort | bioinformatics prediction of function of t cell exhaustion related genes in ischemic stroke |
| topic | Ischemic stroke T cell exhaustion Biomarkers Machine learning |
| url | https://doi.org/10.1038/s41598-025-03724-y |
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