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首页> 外文期刊>Journal of International Medical Research >Inflammation as a risk factor for stroke in atrial fibrillation: data from a microarray data analysis
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Inflammation as a risk factor for stroke in atrial fibrillation: data from a microarray data analysis

机译:心房颤动中风​​的炎症是中风的危险因素:来自微阵列数据分析的数据

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Objective Stroke is a severe complication of atrial fibrillation (AF). We aimed to discover key genes and microRNAs related to stroke risk in patients with AF using bioinformatics analysis. Methods GSE66724 microarray data, including peripheral blood samples from eight patients with AF and stroke and eight patients with AF without stroke, were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AF patients with and without stroke were identified using the GEO2R online tool. Functional enrichment analysis was performed using the DAVID database. A protein–protein interaction (PPI) network was obtained using the STRING database. MicroRNAs (miRs) targeting these DEGs were obtained from the miRNet database. A miR–DEG network was constructed using Cytoscape software. Results We identified 165 DEGs (141 upregulated and 24 downregulated). Enrichment analysis showed enrichment of certain inflammatory processes. The miR–DEG network revealed key genes, including MEF2A , CAND1 , PELI1 , and PDCD4 , and microRNAs, including miR-1, miR-1-3p, miR-21, miR-21-5p, miR-192, miR-192-5p, miR-155, and miR-155-5p. Conclusion Dysregulation of certain genes and microRNAs involved in inflammation may be associated with a higher risk of stroke in patients with AF. Evaluating these biomarkers could improve prediction, prevention, and treatment of stroke in patients with AF.
机译:目的中风是心房颤动的严重并发症(AF)。我们的旨在发现使用生物信息学分析的AF患者患者中风风险相关的主要基因和微小卷曲。方法从基因表达综合(GEO)数据库下载GSE66724 Microbray数据,包括来自AF和中风和中风和中风的患者的外周血样品和八名患有AF的AF的患者。使用GEO2R在线工具鉴定AF与没有中风的差异表达的基因(DEGS)。使用David数据库进行功能丰富分析。使用串数据库获得蛋白质 - 蛋白质相互作用(PPI)网络。针对这些DEG的MicroRNAS(MIRS)从Mirnet数据库获得。使用Cytoscape软件构建MIR-DEG网络。结果我们确定了165次(141个上调和24个下调)。富集分析表明某些炎症过程的富集。 MiR-DEG网络揭示了关键基因,包括MEF2A,CAND1,PELI1和PDCD4,以及MICRRNA,包括MIR-1,MIR-1-3P,MIR-21,MIR-21-5P,MIR-192,MIR-192 -5p,miR-155和miR-155-5p。结论某些基因的失调和参与炎症的微血管可能与AF患者患者中风的风险较高有关。评估这些生物标志物可以改善AF的患者中风的预测,预防和治疗。

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