摘要
目的利用生物信息学方法筛查肺腺癌的差异基因,分析其在肺腺癌的发生发展过程中可能参与的信号传导通路,寻找肺腺癌的关键基因并评估其对肺腺癌预后的意义。方法从GEO数据库中获取肺腺癌基因表达芯片数据集GSE10072、GSE32863、GSE43458和GSE116959,将四组数据集整合后获得肺腺癌的差异表达基因,采用STRING数据库对差异表达基因构建肺腺癌蛋白-蛋白互相作用网络,通过在线网站DAVID对差异基因进行GO富集分析和KEGG通路分析,用Cytohubba筛选关键基因,并利用GEPIA分析关键基因与预后的相关性。结果初步筛查得到214个差异基因,包括42个上调基因和172个下调基因,最后筛选得到6个关键基因。生存分析显示PECAM1、SPP1和KIAA0101的表达对肺腺癌的预后有显著影响(P<0.05),Diseasemeth分析显示SPP1、KIAA0101、COL3A1、GNG11和FOS基因在肺腺癌组织中的甲基化水平异常(P<0.05)。结论这6个基因可能参与了肺腺癌的发生发展,对肺腺癌的诊断、靶点治疗和预后提供一定参考。
Objective To screen the differentially expressed genes(DEGs)of lung adenocarcinoma with Bioinformatics and to analyze the possible signal transduction pathways involved in the occurrence and development of lung adenocarcinoma,so as to find the key genes and evaluate the prognostic significance of lung adenocarcinoma.Methods The gene expression profile dataset of lung adenocarcinoma GSE10072,GSE32863,GSE43458 and GSE116959 were downloaded from the GEO database.These 4 datasets were integrated to obtain DEGs related to lung adenocarcinoma.STRING database was used to construct the protein-protein interaction(PPI)network,GO and KEGG analysis were conducted by the online website DAVID.The key genes were screened through Cytohubba.GEPIA was applied to analyze the correlation between key genes and prognosis.Results 214 DEGs were obtained from the primary screen,including 42 up-regulated genes and 172 down-regulated genes,and 6 key genes were screened ultimately.The survival analysis showed that the expression of PECAM1,SPP1 and KIAA0101 had a significant effect on the prognosis of lung adenocarcinoma(P<0.05),and the Diseasemeth analysis indicated that the methylation level of SPP1,KIAA0101,COL3 A1,GNG11 and FOS was abnormal(P<0.05).Conclusion The 6 genes are likely to regulate and control the occurrence and development of lung adenocarcinoma,this result can be served as reference for the diagnosis,target treatment and prognosis of lung adenocarcinoma.
作者
马国玉
熊庆
蒋国庆
杨家甜
木云珍
MA Guo-yu;XIONG Qing;JIANG Guo-qing;YANG Jia-tian;MU Yun-zhen(School of Public Health,Kunming Medical University,Kunming Yunnan 650500;Institute of Environmental Health,Yunnan Center for Disease Control and Prevention,Kunming Yunnan 650022;Dept.of Obstetrics,The 1st Affiliated Hospital of Kunming Medical University,Kunming Yunnan 650032,China)
出处
《昆明医科大学学报》
CAS
2020年第7期30-37,共8页
Journal of Kunming Medical University
基金
昆明医科大学科技创新团队基金资助项目(CXTD201706)。
关键词
生物信息学
肺腺癌
基因
生存分析
预后
Bioinformatics
Lung adenocarcinoma
Gene
Survival analysis
Prognosis
