胰腺癌关键基因的筛选和分析:多数据联合生物信息学分析

目的 通过多数据联合生物信息学分析,明确候选基因与胰腺癌发生发展以及预后的相关关系.方法 从Gene Expression Omnibus(GEO)下载芯片数据集GSE15471、GSE16515、GSE28735,筛选差异表达基因.通过功能富集分析,利用STRING和Cytoscape软件构建蛋白质相互作用(prot...

Screening and analysis of key genes in pancreatic cancer:multi-data combined bioinformatics analysis

Objective To identify the correlation between candidate genes and the development,progression and prognosis of pancreatic cancer by multi-data combined bioinformatics analysis.Methods From Gene Expression Omnibus(GEO)download the microarray data set GSE15471,GSE16515,GSE28735,screening of differentially expressed genes.Through functional enrichment analysis,the protein-protein interaction(PPI)network was constructed by STRING and Cytoscape software.Enrichment functions and pathways included:biological regulation,metabolic process,stimulus response,cell signaling,cell proliferation,growth,ECM-receptor interaction,PI3K-Akt signaling pathway,pancreatic secretion,renin-angiotensin system,complement and coagulation cascade.Results A total of 247 differentially expressed genes were screened,of which 190 were up-regulated and 57 were down-regulated.The key genes of the eight modules identified by Cytoscape were COMP,ANLN,VCAN,DDX60,ERP27,MET,NR5A2,and C5.Finally,the role of hub genes in tumor was comprehensively analyzed through multi-database analysis,and survival analysis showed that ANLN,MET,DDX60 and ERP27 may play an important role in tumor invasion,recurrence and metastasis.Conclusion The screening of differentially expressed genes and key modules reveals the potential genes that promote tumorigenesis and development,and provides a new target for diagnosis and treatment.