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Abstract：
BACKGROUND: Both ischemic stroke (IS) and myocardial infarction (MI) are caused by vascular occlusion that results in ischemia. While there may be similarities in their mechanisms, the potential relationship between these 2 diseases has not been comprehensively analyzed. Therefore, this study explored the commonalities in the pathogenesis of IS and MI.
METHODS: Datasets for IS (GSE58294, GSE16561) and MI (GSE60993, GSE61144) were downloaded from the Gene Expression Omnibus database. Transcriptome data from each of the 4 datasets were analyzed using bioinformatics, and the differentially expressed genes (DEGs) shared between IS and MI were identified and subsequently visualized using a Venn diagram. A protein-protein interaction (PPI) network was constructed using the Interacting Gene Retrieval Tool database, and identification of key core genes was performed using CytoHubba. Gene Ontology (GO) term annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the shared DEGs were conducted using prediction and network analysis methods, and the functions of the hub genes were determined using Metascape.
RESULTS: The analysis revealed 116 and 1321 DEGs in the IS and MI datasets, respectively. Of the 75 DEGs shared between IS and MI, 56 were upregulated and 19 were downregulated. Furthermore, 15 core genes - S100a12, Hp, Clec4d, Cd163, Mmp9, Ormdl3, Il2rb, Orm1, Irak3, Tlr5, Lrg1, Clec4e, Clec5a, Mcemp1, and Ly96 - were identified. GO enrichment analysis of the DEGs showed that they were mainly involved in the biological functions of neutrophil degranulation, neutrophil activation during immune response, and cytokine secretion. KEGG analysis showed enrichment in pathways pertaining to Salmonella infection, Legionellosis, and inflammatory bowel disease. Finally, the core gene-transcription factor, gene-microRNA, and small-molecule relationships were predicted.
CONCLUSIONS: These core genes may provide a novel theoretical basis for the diagnosis and treatment of IS and MI.
METHODS: Datasets for IS (GSE58294, GSE16561) and MI (GSE60993, GSE61144) were downloaded from the Gene Expression Omnibus database. Transcriptome data from each of the 4 datasets were analyzed using bioinformatics, and the differentially expressed genes (DEGs) shared between IS and MI were identified and subsequently visualized using a Venn diagram. A protein-protein interaction (PPI) network was constructed using the Interacting Gene Retrieval Tool database, and identification of key core genes was performed using CytoHubba. Gene Ontology (GO) term annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the shared DEGs were conducted using prediction and network analysis methods, and the functions of the hub genes were determined using Metascape.
RESULTS: The analysis revealed 116 and 1321 DEGs in the IS and MI datasets, respectively. Of the 75 DEGs shared between IS and MI, 56 were upregulated and 19 were downregulated. Furthermore, 15 core genes - S100a12, Hp, Clec4d, Cd163, Mmp9, Ormdl3, Il2rb, Orm1, Irak3, Tlr5, Lrg1, Clec4e, Clec5a, Mcemp1, and Ly96 - were identified. GO enrichment analysis of the DEGs showed that they were mainly involved in the biological functions of neutrophil degranulation, neutrophil activation during immune response, and cytokine secretion. KEGG analysis showed enrichment in pathways pertaining to Salmonella infection, Legionellosis, and inflammatory bowel disease. Finally, the core gene-transcription factor, gene-microRNA, and small-molecule relationships were predicted.
CONCLUSIONS: These core genes may provide a novel theoretical basis for the diagnosis and treatment of IS and MI.
摘要：
背景：缺血性卒中（IS）和心肌梗塞（MI）均由导致缺血的血管闭塞引起。虽然它们的机制可能有相似之处，这两种疾病之间的潜在关系尚未得到全面分析。因此,本研究探讨了IS和MI发病机制的共性。
方法:从基因表达综合数据库下载IS(GSE58294,GSE16561)和MI(GSE60993,GSE61144)的数据集。使用生物信息学分析了4个数据集的转录组数据，并鉴定了IS和MI之间共享的差异表达基因（DEGs），随后使用维恩图进行可视化。使用相互作用基因检索工具数据库构建了蛋白质-蛋白质相互作用（PPI）网络，并使用CytoHubba进行关键核心基因的鉴定。使用预测和网络分析方法对共享的DEGs进行了基因本体（GO）术语注释和京都基因和基因组百科全书（KEGG）途径富集分析，使用Metascape确定了hub基因的功能。
结果：分析显示IS和MI数据集中有116和1321DEG，分别。在IS和MI之间共享的75个DEG中，56个上调，19个下调。此外，15个核心基因-S100a12，Hp，Clec4d,Cd163,Mmp9,Ormdl3,Il2rb,Orm1,Irak3,Tlr5,Lrg1,Clec4e,Clec5a,确定了Mcemp1和Ly96。GO富集分析表明,它们主要参与中性粒细胞脱颗粒的生物学功能,免疫反应过程中的中性粒细胞激活，和细胞因子分泌。KEGG分析显示与沙门氏菌感染有关的途径富集，军团菌病,和炎症性肠病.最后,核心基因转录因子，基因-microRNA,并预测了小分子关系。
结论：这些核心基因可能为IS和MI的诊断和治疗提供了新的理论基础。
方法:从基因表达综合数据库下载IS(GSE58294,GSE16561)和MI(GSE60993,GSE61144)的数据集。使用生物信息学分析了4个数据集的转录组数据，并鉴定了IS和MI之间共享的差异表达基因（DEGs），随后使用维恩图进行可视化。使用相互作用基因检索工具数据库构建了蛋白质-蛋白质相互作用（PPI）网络，并使用CytoHubba进行关键核心基因的鉴定。使用预测和网络分析方法对共享的DEGs进行了基因本体（GO）术语注释和京都基因和基因组百科全书（KEGG）途径富集分析，使用Metascape确定了hub基因的功能。
结果：分析显示IS和MI数据集中有116和1321DEG，分别。在IS和MI之间共享的75个DEG中，56个上调，19个下调。此外，15个核心基因-S100a12，Hp，Clec4d,Cd163,Mmp9,Ormdl3,Il2rb,Orm1,Irak3,Tlr5,Lrg1,Clec4e,Clec5a,确定了Mcemp1和Ly96。GO富集分析表明,它们主要参与中性粒细胞脱颗粒的生物学功能,免疫反应过程中的中性粒细胞激活，和细胞因子分泌。KEGG分析显示与沙门氏菌感染有关的途径富集，军团菌病,和炎症性肠病.最后,核心基因转录因子，基因-microRNA,并预测了小分子关系。
结论：这些核心基因可能为IS和MI的诊断和治疗提供了新的理论基础。
