Is Immune Suppression Involved in the Ischemic Stroke? A Study Based on Computational Biology
ObjectiveTo identify the genetic mechanisms of immunosuppression-related genes implicated in ischemic stroke.BackgroundA better understanding of immune-related genes (IGs) involved in the pathophysiology of ischemic stroke may help identify drug targets beneficial for immunomodulatory approaches and...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-02-01
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| Series: | Frontiers in Aging Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2022.830494/full |
| _version_ | 1818897744032432128 |
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| author | Xin Wang Qian Wang Kun Wang Qingbin Ni Hu Li Zhiqiang Su Yuzhen Xu |
| author_facet | Xin Wang Qian Wang Kun Wang Qingbin Ni Hu Li Zhiqiang Su Yuzhen Xu |
| author_sort | Xin Wang |
| collection | DOAJ |
| description | ObjectiveTo identify the genetic mechanisms of immunosuppression-related genes implicated in ischemic stroke.BackgroundA better understanding of immune-related genes (IGs) involved in the pathophysiology of ischemic stroke may help identify drug targets beneficial for immunomodulatory approaches and reducing stroke-induced immunosuppression complications.MethodsTwo datasets related to ischemic stroke were downloaded from the GEO database. Immunosuppression-associated genes were obtained from three databases (i.e., DisGeNET, HisgAtlas, and Drugbank). The CIBERSORT algorithm was used to calculate the mean proportions of 22 immune-infiltrating cells in the stroke samples. Differential gene expression analysis was performed to identify the differentially expressed genes (DEGs) involved in stroke. Immunosuppression-related crosstalk genes were identified as the overlapping genes between ischemic stroke-DEGs and IGs. Feature selection was performed using the Boruta algorithm and a classifier model was constructed to evaluate the prediction accuracy of the obtained immunosuppression-related crosstalk genes. Functional enrichment analysis, gene-transcriptional factor and gene-drug interaction networks were constructed.ResultsTwenty two immune cell subsets were identified in stroke, where resting CD4 T memory cells were significantly downregulated while M0 macrophages were significantly upregulated. By overlapping the 54 crosstalk genes obtained by feature selection with ischemic stroke-related genes obtained from the DisGenet database, 17 potentially most valuable immunosuppression-related crosstalk genes were obtained, ARG1, CD36, FCN1, GRN, IL7R, JAK2, MAFB, MMP9, PTEN, STAT3, STAT5A, THBS1, TLR2, TLR4, TLR7, TNFSF10, and VASP. Regulatory transcriptional factors targeting key immunosuppression-related crosstalk genes in stroke included STAT3, SPI1, CEPBD, SP1, TP53, NFIL3, STAT1, HIF1A, and JUN. In addition, signaling pathways enriched by the crosstalk genes, including PD-L1 expression and PD-1 checkpoint pathway, NF-kappa B signaling, IL-17 signaling, TNF signaling, and NOD-like receptor signaling, were also identified.ConclusionPutative crosstalk genes that link immunosuppression and ischemic stroke were identified using bioinformatics analysis and machine learning approaches. These may be regarded as potential therapeutic targets for ischemic stroke. |
| first_indexed | 2024-12-19T19:21:02Z |
| format | Article |
| id | doaj.art-b9b5f5f1a2b0498a91d593632ddd26fa |
| institution | Directory Open Access Journal |
| issn | 1663-4365 |
| language | English |
| last_indexed | 2024-12-19T19:21:02Z |
| publishDate | 2022-02-01 |
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| series | Frontiers in Aging Neuroscience |
| spelling | doaj.art-b9b5f5f1a2b0498a91d593632ddd26fa2022-12-21T20:09:00ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652022-02-011410.3389/fnagi.2022.830494830494Is Immune Suppression Involved in the Ischemic Stroke? A Study Based on Computational BiologyXin Wang0Qian Wang1Kun Wang2Qingbin Ni3Hu Li4Zhiqiang Su5Yuzhen Xu6Department of Neurology, First Affiliated Hospital of Harbin Medical University, Harbin, ChinaPostdoctoral Workstation, Taian City Central Hospital, Taian, ChinaPostdoctoral Workstation, Taian City Central Hospital, Taian, ChinaPostdoctoral Workstation, Taian City Central Hospital, Taian, ChinaDepartment of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian, ChinaDepartment of Neurology, First Affiliated Hospital of Harbin Medical University, Harbin, ChinaDepartment of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian, ChinaObjectiveTo identify the genetic mechanisms of immunosuppression-related genes implicated in ischemic stroke.BackgroundA better understanding of immune-related genes (IGs) involved in the pathophysiology of ischemic stroke may help identify drug targets beneficial for immunomodulatory approaches and reducing stroke-induced immunosuppression complications.MethodsTwo datasets related to ischemic stroke were downloaded from the GEO database. Immunosuppression-associated genes were obtained from three databases (i.e., DisGeNET, HisgAtlas, and Drugbank). The CIBERSORT algorithm was used to calculate the mean proportions of 22 immune-infiltrating cells in the stroke samples. Differential gene expression analysis was performed to identify the differentially expressed genes (DEGs) involved in stroke. Immunosuppression-related crosstalk genes were identified as the overlapping genes between ischemic stroke-DEGs and IGs. Feature selection was performed using the Boruta algorithm and a classifier model was constructed to evaluate the prediction accuracy of the obtained immunosuppression-related crosstalk genes. Functional enrichment analysis, gene-transcriptional factor and gene-drug interaction networks were constructed.ResultsTwenty two immune cell subsets were identified in stroke, where resting CD4 T memory cells were significantly downregulated while M0 macrophages were significantly upregulated. By overlapping the 54 crosstalk genes obtained by feature selection with ischemic stroke-related genes obtained from the DisGenet database, 17 potentially most valuable immunosuppression-related crosstalk genes were obtained, ARG1, CD36, FCN1, GRN, IL7R, JAK2, MAFB, MMP9, PTEN, STAT3, STAT5A, THBS1, TLR2, TLR4, TLR7, TNFSF10, and VASP. Regulatory transcriptional factors targeting key immunosuppression-related crosstalk genes in stroke included STAT3, SPI1, CEPBD, SP1, TP53, NFIL3, STAT1, HIF1A, and JUN. In addition, signaling pathways enriched by the crosstalk genes, including PD-L1 expression and PD-1 checkpoint pathway, NF-kappa B signaling, IL-17 signaling, TNF signaling, and NOD-like receptor signaling, were also identified.ConclusionPutative crosstalk genes that link immunosuppression and ischemic stroke were identified using bioinformatics analysis and machine learning approaches. These may be regarded as potential therapeutic targets for ischemic stroke.https://www.frontiersin.org/articles/10.3389/fnagi.2022.830494/fullimmunosuppressionischemic strokegenestranscription factorsbioinformatics |
| spellingShingle | Xin Wang Qian Wang Kun Wang Qingbin Ni Hu Li Zhiqiang Su Yuzhen Xu Is Immune Suppression Involved in the Ischemic Stroke? A Study Based on Computational Biology Frontiers in Aging Neuroscience immunosuppression ischemic stroke genes transcription factors bioinformatics |
| title | Is Immune Suppression Involved in the Ischemic Stroke? A Study Based on Computational Biology |
| title_full | Is Immune Suppression Involved in the Ischemic Stroke? A Study Based on Computational Biology |
| title_fullStr | Is Immune Suppression Involved in the Ischemic Stroke? A Study Based on Computational Biology |
| title_full_unstemmed | Is Immune Suppression Involved in the Ischemic Stroke? A Study Based on Computational Biology |
| title_short | Is Immune Suppression Involved in the Ischemic Stroke? A Study Based on Computational Biology |
| title_sort | is immune suppression involved in the ischemic stroke a study based on computational biology |
| topic | immunosuppression ischemic stroke genes transcription factors bioinformatics |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2022.830494/full |
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