基于WGCNA构建食管鳞癌免疫相关基因预后模型

Development of a prognostic model of immune-related genes for esophageal squamous cell carcinoma based on WGCNA

目的:采用加权基因共表达网络分析(WGCNA)筛选食管鳞癌(ESCC)预后相关免疫相关基因(IRG),构建预后模型,为ESCC患者预后预测提供依据。方法:从TCGA数据库下载81例ESCC及11例正常食管组织的全转录组数据及对应样本临床数据,筛选差异表达IRGs。采用WGCNA、单因素Cox回归和多因素Cox回归分析构建ESCC预后模型,绘制受试者工作特征曲线(ROC)及Kaplan-Meier(K-M)曲线评价模型,单因素Cox回归和多因素Cox回归分析评价模型是否可成为独立预测因子。结果:筛选出545个差异表达IRGs,其中383个上调,162个下调。单因素Cox回归分析得到6个预后相关IRGs,多因素Cox回归分析得到4个预后相关IRGs(SLC40A1、IGHA2、STC2、NR2F2)用于构建预后风险模型。K-M曲线显示,高风险组患者总生存期(OS)显著低于低风险组(P<0.01)。多因素Cox回归分析表明,预后风险模型可作为一个独立预后因素(HR=1.133,P<0.01)。结论:本模型具有较高的准确性,可作为ESCC患者预后的独立预测因子。

Objective:To screen prognostic immune related genes(IRG)of esophageal carcinoma(ESCC)by weighted gene co-expression network analysis(WGCNA),and to establish a prognostic model to provide references for prognostic prediction of ESCC patients.Methods:Genome-wide data of 81 ESCC and 11 normal esophageal tissues and clinical data of corresponding samples were downloaded from TCGA database to screen differentially expressed IRGs.Prognostic model of ESCC was established by WGCNA,uni-variate Cox regression and multivariate Cox regression analysis,and receiver operating characteristic curve(ROC)and Kaplan-Meier(K-M)curve were used to analyze predictive ability of prognostic model.Univariate Cox regression and multivariate Cox regression analysis were used to evaluate whether the models could be independent predictors.Results:A total of 545 differentially expressed IRGs were screened out,of which 383 were up-regulated and 162 were down-regulated.Six prognostic IRGs were obtained by univariate Cox regression analysis,and 4 prognostic IRGs(SLC40A1,IGHA2,STC2,NR2F2)were obtained by multivariate Cox regression analysis,which were used to construct prognostic risk model.K-M curve showed that overall survival(OS)of patients in high-risk group was significantly lower than that in low-risk group(P<0.01).Multivariate Cox regression analysis showed that prognostic risk model could be used as an independent prognostic factor(HR=1.133,P<0.01).Conclusion:The model has high accuracy and can be used as an independent prognostic factor for patients with ESCC.