利用生物信息学分析鉴定与骨关节炎相关的潜在核心基因

Analysis and identification of potential core genes associated with osteoarthritis using bioinformatics

目的:初步探索骨关节炎病理进程中可能涉及的信号通路,筛选与骨关节炎发生相关的关键基因,从而揭示骨关节炎的发病机制。方法:从公共基因芯片数据库(Gene Expression Omnibus,GEO)下载骨关节炎相关芯片数据(GSE19060),利用GEO2R工具筛选骨关节炎(osteoarthritis,OA)和正常组织之间的差异表达基因(Differentially expressed genes,DEGs),用GO分析和KEGG信号通路分析分别对筛选得到的DEGs进行功能注释,之后构建蛋白质-蛋白质相互作用(Protein-protein interaction,PPI)网络,由Cytoscape软件将DEGs可视化,并筛选核心关键基因。结果:共筛选出106个差异表达基因,其中52个上调表达,54个下调表达。GO分析表明差异表达基因生物学功能主要涉及细胞粘附、细胞增殖的负调控和细胞因子调控的信号通路。KEGG分析表明差异表达基因主要和PI3K-Akt信号通路、肌动蛋白细胞骨架调节有关。利用PPI网络在其中挑选出了SFRP4、SFRP1、ITGB2、EPHA4、ELN、IGFBP5、FMOD、SYNPO2、PITX2、DSP等10个连接度最高的与骨关节炎发病机制相关的核心基因。结论:生物信息学分析能有效分析和筛选骨关节炎差异表达基因,获取生物内在相关信息,并为进一步探索骨关节炎发病机制提供理论依据。

Objective:To preliminarily explore the signal pathways that may be involved in the pathological process of osteoarthritis,and screen the key genes related to the occurrence of osteoarthritis,so as to reveal the pathogenesis of osteoarthritis.Methods:Microarray datasets(GSE19060)derived from the Gene Expression Omnibus(GEO)database was downloaded,the GEO2R tool was used to screen out differentially expressed genes(DEGs)between osteoarthritis(OA)and normal tissue.Gene Ontology function(GO)and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis(KEGG)were performed using the Database for Annotation,then Cytoscape software was used to visualized DEGs,protein-protein interaction network was built,and core key genes were filtered.Results:A total of 106 differentially expressed genes were selected,among which 52 were up-regulated and 54 were down-regulated.GO analysis showed that the biological functions of differentially expressed genes were mainly involved in cell adhesion,negative regulation of cell proliferation and cytokine-mediated signaling pathway.KEGG analysis showed that differentially expressed genes were mainly related to the regulation of PI3K-Akt signaling pathway and regulation of acting cytoskeleton.Among them,10 core genes with the highest degree of connection related to the pathogenesis of osteoarthritis were selected using PPI construction,including SFRP4,SFRP1,ITGB2,EPHA4,ELN,IGFBP5,FMOD,SYNPO2,PITX2 and DSP.Conclusion:Bioinformatics analysis can effectively analyze and screen genes differentially expressed in osteoarthritis,obtain relevant biological information,and provide theoretical basis for further exploration of the pathogenesis of osteoarthritis.