Is Immune Suppression Involved in the Ischemic Stroke? A Study Based on Computational Biology.
Is Immune Suppression Involved in the Ischemic Stroke? A Study Based on Computational Biology.
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DOI：
10.3389/fnagi.2022.830494
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发表时间：
2022
影响因子：
4.8
通讯作者：
Xu Y
中科院分区：
文献类型：
--
作者：
Wang X;Wang Q;Wang K;Ni Q;Li H;Su Z;Xu Y
探讨免疫抑制相关基因在缺血性卒中发病机制中的作用。更好地了解参与缺血性卒中病理生理学的免疫相关基因(IGs)可能有助于确定有利于免疫调节途径和减少卒中诱导的免疫抑制并发症的药物靶点。从GEO数据库下载了两个与缺血性中风相关的数据集。免疫抑制相关基因从三个数据库(即DisGeNET、HisgAtlas和DrugBank)获得。CiberSort算法被用来计算中风样本中22个免疫渗透细胞的平均比例。进行差异基因表达分析以确定与卒中相关的差异表达基因(DEG)。免疫抑制相关的串扰基因被鉴定为缺血性卒中患者和免疫球蛋白患者之间的重叠基因。使用Boruta算法进行特征选择，并构建分类器模型来评估所获得的免疫抑制相关串扰基因的预测精度。构建了功能浓缩分析网络、基因转录因子网络和基因药物相互作用网络。在中风患者中发现了22个免疫细胞亚群，其中静息的CD4T记忆细胞显著下调，而M0巨噬细胞显著上调。通过将特征选择获得的54个串扰基因与DisGenet数据库中获得的缺血性卒中相关基因重叠，获得了17个潜在最有价值的免疫抑制相关串扰基因：ARG1、CD36、FCN1、GRN、IL7R、JAK2、MAFB、MMP9、PTEN、STAT3、STAT5A、THBS1、TLR2、TLR4、TLR7、TNFSF10和Vasp。针对卒中关键免疫抑制相关串扰基因的转录调控因子包括STAT3、SPI1、CEPBD、SP1、TP53、NFIL3、STAT1、HIF1a和Jun。此外，还鉴定了由串扰基因丰富的信号通路，包括PD-L1表达和PD-1检查点通路、NF-kappa B信号、IL-17信号、肿瘤坏死因子信号和Nod样受体信号。使用生物信息学分析和机器学习方法确定了可能的串扰基因，这些基因与免疫抑制和缺血性中风有关。这些可能被视为治疗缺血性卒中的潜在靶点。
To identify the genetic mechanisms of immunosuppression-related genes implicated in ischemic stroke. A 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. Two 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. Twenty 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. Putative 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.
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影响因子：
2.7
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Diao X;Liu A
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