通过加权基因共表达网络分析法探索胰腺癌特异表达的关键基因及表达网络

目的 通过使用加权基因共表达网络分析(WGCNA)法研究胰腺癌潜在的分子机制,并寻找关键基因。方法 从肿瘤基因组图谱(TCGA)数据库中获得胰腺癌和对照组的基因表达数据。通过limma包在R中识别差异表达的基因(DEGs)。采用WGCNA构建胰腺癌的基因共表达网络,并识别共表达模块,进行蛋白质相互作用(PPI)分析,及进行关键基因的筛选。结果 共鉴定出胰腺癌106个DEGs,通过WGCNA分析,确定了一个关键模块(MEpurple)。进一步筛选出10个关键基因,包括PKP3, EPCAM, RAB25, CBLC, AP1M2, PRP15L, B3GNT3, ESRP1, AGR2, ARHGEF16。结论 WGCNA法可以识别出与胰腺癌相关的模块和基因,为进一步研究胰腺癌发生发展的分子机制以及靶向治疗提供理论依据。

Identification of differently expressed hub genes in pancreatic cancer by weighted gene co-expression network analysis

Objective To identify the potential key genes and molecular mechanism of pancreatic cancer by weighted gene co-expression network analysis (WGCNA).Methods The gene data of pancreatic cancer and control group were obtained from the cancer genome atlas (TCGA) database. Differentially expressed genes (DEGs) were identified by limma package in R. Then, WGCNA was used to construct the gene co-expression network of pancreatic cancer, identify the co-expression modules, analyze the protein- protein interaction (PPI) and screen the key genes. Results Totally 106 DEGs were identified, and a key module (MEpurple) was identified by WGCNA analysis. We further screened 10 key genes, including PKP3, EPCAM, RAB25, CBLC, AP1M2, PRP15L, B3GNT3, ESRP1, AGR2, ARHGEF16.Conclusion By using WGCNA algorithm, we have identified the modules and genes related to pancreatic cancer, which provides a theoretical basis for further research on the molecular mechanism of pancreatic cancer development and treatment.