生物信息学分析在非小细胞肺癌关键通路和基因鉴定中的应用

Identification of key pathways and genes in non-small cell lung cancer using bioinformatics analysis

目的通过高通量平台筛选差异表达基因间的相互联系和其相互作用的途径。方法从高通量基因表达数据库(GEO)下载原始数据,比较非小细胞肺癌患者与健康样本的基因表达谱,确定差异表达基因(Differential Gene Expression,DEGs)和差异表达的microRNAs(DEMs)。随后利用GeneSpring软件筛选了DEGs,并进行基因本体论(GO)和关键基因和基因组百科库(KEGG)通路富集分析。结果共筛选出460个DEGs和25个DEMs,其中,CCNB1、CDK1、CDC45、CCNB2、BUB1、CD36和几种miRNAs(例如miR-9和miR-451)可能是与NSCLC有关的关键基因。结论数据挖掘与整合是预测NSCLC发生、发展的有用工具,有助于进一步了解肿瘤发生发展的机制。

Purpose This study aims to screen the interaction between differentially expressed genes and their interaction pathways through high-throughput platforms.Methods In the present study,we downloaded the original data from Gene Expression Omnibus(GEO).Gene expression profiles of cancer cells in patients with NSCLC were compared with those in healthy colon epitheliums to identify the differentially expressed genes(DEGs)and microRNAs(DEMs).Subsequently,the DEGs were screened using GeneSpring software,followed by gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis.Results In our study,a total of 460 DEGs and 25 DEMs were screened out,and CCNB1,CDK1,CDC45,CCNB2,BUB1,CD36 and several miRNAs such as miR-9 and miR-451 might be key genes related to NSCLC.Conclusions Our results suggested that data mining and integration could be a useful tool to predict progression of NSCLC and to understand the mechanism of the occurrence and development of tumor.