非小细胞肺癌关键基因的生物信息学分析

目的:通过生物信息学方法挖掘非小细胞肺癌(NSCLC)基因表达谱芯片数据,筛选并验证与NSCLC发生和预后相关的关键基因.方法:从Gene Expression Omnibus(GEO)数据库中下载芯片数据(GSE101929和GSE27262).采用GEO2R在线工具筛选癌组织和癌旁组织中的差异表达基因(DEGs);...

Bioinformatics analysis of key genes associated with non-small-cell lung cancer by using published microarray datasets

Objective:To identify and validate key genes associated with non-small-cell lung cancer(NSCLC)by using bioinformatics from two mRNA microarray datasets(GSE101929 and GSE27262)from the Gene Expression Omnibus(GEO)database.Methods:Differential expression genes(DEGs)were obtained from GEO2R online tool and the overlap DEGs were identified.GO analysis and KEGG pathway enrichment were performed by DAVID database and visualized by Cytoscape and FunRich software.Finally,GEPIA was employed to validate the hub genes and analyze the overall survival(OS)of NSCLC patients.Results:A total of 651 up-regulated and 1165 down-regulated overlap DEGs were obtained.The up-regulated DEGs were mainly enriched in molecular function of metallopeptidase activity,while down-regulated genes were mainly enriched in receptor activity.KEGG analysis showed that the up-regulated genes mainly enriched in mitotic prometaphase signaling pathway,while the down-regulated genes mainly enriched in epithelial to mesenchymal transition signaling pathway.The PPI network showed that the top 5 up-regulated genes were TOP2A,CDK1,CCNB1,CCNA2 and KIF11,while the top 5 down-regulated genes were IL6,FGF2,LRRK2,EDN1 and IL1B.The overall survival(OS)analysis revealed that only the up-regulated gene KIF11 was associated with worse OS of patients with NSCLC.Conclusion:The present study identified key genes associated with NSCLC,which might be used as prognostic biomarkers and therapeutic targets of NSCLC.