生信分析筛选心衰氧化应激相关关键[]基因并预测候选药物

为探究氧化应激相关基因在心力衰竭发生发展中的作用,并发现核心基因进行靶基因药物预测。从GEO数据库下载GSE120895基因表达图谱,通过GEO2R筛选差异表达基因,将差异表达基因与GeneCard数据库中筛选的氧化应激相关基因取交集,得到心力衰竭氧化应激相关差异表达基因,利用R软件对差异表达基因进行GO及KEGG分析,利用Cytoscape进行PPI网络的模块以及关键基因的筛选。之后在GSE17800基因表达图谱中验证关键基因的表达,并针对关键基因进行相互作用药物预测。差异表达基因与氧化应激相关基因取交集后,共筛选出52个上调的氧化应激相关差异表达基因,在此基础上,筛选出ACTB,STAT3,FN1,EDN1,CAT共5个关键基因,在GSE17800基因表达图谱中验证后,针对4个关键基因预测了19个靶基因潜在药物。总之,本研究通过生物信息学方法鉴定关键基因,并预测潜在治疗药物,从而为了解心力衰竭的分子机制及其诊治方法提供新的见解。

Screening of hub genes related to oxidative stress in heart failure and candidatedrugs prediction based on bioinformatics analysis

HJ1.4mm]To identify major genes and pathways involved in oxidative stress in heart failure by bioinformatics analysis. GSE120895 gene expression profiles are acquired from the GEO database. And GEO2R is used to identify differentially expressed genes. We intersect the differentially expressed genes with the oxidative stress-related genes screened in the GeneCard database to find the oxidative stress-related genes in heart failure. R software is used to conduct GO and KEGG analyses of differentially expressed genes. Module of PPI network and analysis of key genes using Cytoscape software. We employ GSE17800 gene expression profiles to confirm the expression of important genes. For important genes, medication interactions are predicted. We identify 52 differentially expressed genes that are up-regulated in response to oxidative stress. Five key genes(ACTB, STAT3, FN1, EDN1,and CAT)are selected as a result. These five genes are validated in the GSE17800 gene expression profile. 19 candidate medicines that might target important genes are subsequently predicted. This study employ bioinformatics to explore for distinct genes that may be important to oxidative stress in heart failure, and help to better understand the pathophysiology of heart failure and generates new therapeutic options.