非小细胞肺癌预后基因的筛选及鉴定

Screening and identification of prognostic genes in non-small cell lung cancer

目的筛选非小细胞肺癌(non-small cell lung cancer,NSCLC)预后的关键基因,并对其进行生物信息学分析及鉴定。方法从公共数据库基因表达数据库(Gene Expression Omnibus,GEO)中下载NSCLC cDNA芯片集GSE19188、GSE101929、GSE40275、GSE18842,利用在线工具GEO2R对差异表达基因(differentially expressed genes,DEGs)进行筛选,并用Venny取交集。基于DAVID数据库对DEGs进行GO(Gene Ontology)分析及KEGG(Kyoto Encyclopedia of Gene and Genome)通路分析,运用STRING数据库及Cytoscape软件构建蛋白互作网络。使用CytoHubba插件鉴定核心关键基因,并对其生存分析和表达检测。结果4个cDNA芯片集中共筛选得到130个差异表达基因,包括53个上调表达基因和77个下调表达基因。这些差异表达的基因主要集中在胞浆和细胞外区域,所参与的主要是细胞周期调控、有丝分裂、微管运动相关信号。筛选出的处于核心地位的20个基因中,9个基因与肺癌患者预后显著相关,包括UBE2C、TTK、CEP55、ASPM、PRC1、CCNB2、CCNA2、CCNB1和CDC6。结论通过生物信息学分析,筛选出与正常组织相比,在NSCLC中异常表达的基因130个,并鉴定出其中与预后显著相关的9个核心基因,对进一步了解NSCLC发生发展的分子机制,筛选鉴定新的预后标记物及潜在性分子靶点具有积极意义。

Objective To screen,analyze the bioinformatics and identify the critically prognostic genes in non-small cell lung cancer(NSCLC).Methods Four different cDNA expression profiles,GSE19188,GSE101929,GSE40275 and GSE18842,were downloaded from Gene Expression Omnibus(GEO)database to screen the differently expressed genes(DEGs)between cancer and normal lung tissues by using online GEO2 R tool,and the genes shared by the four profiles were identified with Venn diagram.The basic functions of these DEGs were explored by using GO and Kyoto Encyclopedia of Gene and Genome(KEGG)of DAVID software.Meanwhile,the protein-protein network of the genes was constructed by using STRING and CYTOSKYPE software,and core genes in the protein-protein network were identified by using CytoHubba,then analyzed for survival by Kaplan Meier plot and for expression level by GEPIA servers.Results A total of 130 DEGs were identified from the 4 profiles,including 53 up-regulated and 77 down-regulated genes.The DEGs were mostly focused in cytoplasm and extracellular space,which mainly participated in the signaling pathways associated with cell cycle,mitosis and tubular activity regulation.Of the 20 core genes screened,9 were significantly associated with the prognosis of patients with NSCLC,including UBE2 C,TTK,CEP55,ASPM,PRC1,CCNB2,CCNA2,CCNB1 and CDC6.Conclusion By using bioinformatic analysis,130 genes of which the expressions were changed in NSCLC compared with those in normal lung tissues were screened,and 9 core genes significantly associated with the prognosis were identified.It provides a useful tool for further analysis of the molecular mechanism of development of NSCLC and screening of new prognostic indicators and potential gene targets.