【摘要】 目的应用生物信息学的方法对骨肉瘤(osteosarcoma,OS)基因表达谱芯片进行分析,从分子水平探讨OS的发病机制。方法从GEO数据库下载OS基因芯片数据集的原始数据,应用生物信息学方法筛选差异表达基因(differentially expressed genes,DEGs),并利用R语言enrichplot软件包对差异基因进行基因本体论(Gene ontology,GO)及通路富集(KEGG)分析,通过STRING在线软件、Cytoscape及其插件cytoHubba、Network Analyzer对OS的显著差异基因进行蛋白质-蛋白质相互作用网络分析(PPI分析),寻找关键(Hub)基因。结果共筛选出408个DEGs,其中包括274个上调基因和134个下调基因。对其进行生物信息学分析发现,SPP1、HLA-DPA1、MAFB、PRAME、RPS11、FOSL1、S100A2、PPID、DHRS2等基因及PI3K-Akt信号通路、趋化因子信号通路、Toll样受体信号通路、p53信号通路在OS的发生发展中起着重要作用。通过STRING分析发现,包括GINS2、RFC3、TRIP13等在内的10个基因处于核心节点位置。结论 CDC6、FEN1、OIP5、GINS2和RFC3可能在OS的发生发展中起重要作用,为研究OS的发病机制提供了新线索。更多还原

【Abstract】 Objective To analyze the gene expression profile of osteosarcoma(OS) using bioinformatics methods and to explore the pathogenesis of OS from the molecular level.Methods The raw data of the OS microarray dataset was downloaded from the GEO database.Deferentially expressed genes(DEGs) were screened using bioinformatics methods.Gene Ontology(GO)analysis and pathway enrichment analysis(KEGG)was performed using the R language enrichplot package for differential genes.Through the STRING online software,Cytoscape and its plug-in cytoHubba,Network Analyzer,protein-protein interaction network analysis(PPI analysis)was performed for significant differential genes of OS,in order to seek the key(Hub) gene.Results A total of 408 DEGs were screened,including 274 up-regulated genes and 134 down-regulated genes.Bioinformatics analysis found that SPP1,HLA-DPA1,MAFB,PRAME,RPS11,FOSL1,S100 A2,PPID,DHRS2 and PI3 K-Akt signaling pathways,chemotactic factor signaling pathways,Toll-like receptor signaling pathways,p53 signaling played important roles in the development of OS.Through the STRING analysis,it was found that 10 genes including GINS2,RFC3,and TRIP13 were located at the core node position.Conclusion CDC6,FEN1,OIP5,GINS2 and RFC3 may play an important role in the development of OS and provide new clues for the study of the pathogenesis of OS.更多还原