基于TCGA构建胃癌免疫相关基因预后风险模型及相关分析

Construction of Immune-related Genes Prognostic Risk Model of Gastric Cancer Based on TCGA and Its Correlation Analysis

目的对TCGA数据库胃癌数据集进行分析,构建基于免疫相关基因的预后风险模型。方法下载TCGA数据库中胃癌组织和癌旁组织中的基因表达数据及患者相关临床资料,进行数据整理、合并、表达差异分析。与ImmPort数据库取交集获得差异表达的免疫相关基因(IRGs),并进行GO功能富集分析和KEGG通路富集分析。按照排除标准,将胃癌样本随机分为Train组(224例)、Test组(111例),利用Train组构建免疫相关基因预后风险模型并用Test组进行检验。将胃癌表达数据按照评估模型分为高、低风险2组,进行免疫浸润,分析2组免疫细胞表达水平的差异。观察2组免疫检查点表达差异及免疫治疗相关结果。结果共得到238个差异表达的IRGs。GO功能富集分析结果示差异表达的IRGs主要参与免疫球蛋白生产、免疫反应分子介质的产生等生物学过程。KEGG通路富集分析结果示差异表达的IRGs主要参与细胞因子-细胞因子受体相互作用、神经活性配体-受体相互作用等通路。通过数据分析,得到6个IRGs(MPO APOH IGHD3-16 CGB5 GHR PRKCG)构建的预后风险模型。Train组和Test组中高风险组生存率明显低于低风险组(P<0.05)。Train组ROC第3年曲线下面积为0.692,Test组ROC第3年曲线下面积为0.658。该模型可作为独立预测因子评估胃癌患者的预后。高、低风险组中幼稚B细胞、活化的记忆CD4+T细胞、静止肥大细胞、活化肥大细胞表达具有明显差异。低风险组PD-L1表达增高,抗PD-1治疗效果更加显著。结论基于TCGA确定了一个胃癌免疫相关基因预后风险模型,可以较好地评估胃癌患者的预后情况并指导个体化治疗。

Objective The TCGA database gastric cancer data set was analyzed to construct a prognostic risk model based on immune-related genes.Methods The gene expression data and patient-related clinical data of gastric cancer tissues and adjacent tissues in the TCGA database were downloaded for data collation,merging,and expression difference analysis.The differentially expressed immune-related genes(IRGs)were obtained by intersection with the ImmPort database,and GO function enrichment analysis and KEGG pathway enrichment analysis were performed.According to the exclusion criteria,gastric cancer samples were randomly divided into Train group(224 cases)and Test group(111 cases).The prognostic risk model of immune-related genes was constructed by Train group and tested by Test group.The expression data of gastric cancer were divided into high and low risk groups according to the evaluation model,and immune infiltration was performed to analyze the differences in the expression levels of immune cells between the 2 groups.The differences in the expression of immune checkpoints between the 2 groups and the results of immunotherapy were observed.Results A total of 238 differentially expressed IRGs were obtained.GO functional enrichment analysis showed that differentially expressed IRGs were mainly involved in biological processes such as immunoglobulin production and production of immune response molecular mediators.KEGG pathway enrichment analysis showed that differentially expressed IRGs were mainly involved in cytokine-cytokine receptor interaction,neuroactive ligand-receptor interaction and other pathways.Through data analysis,a prognostic risk model constructed by 6 IRGs(MPO APOH IGHD3-16 CGB5 GHR PRKCG)was obtained.The survival rate of the high-risk group in the Train group and the Test group was significantly lower than that in the low-risk group(P<0.05).The area under the ROC curve in the third year of the Train group was 0.692,and the area under the ROC curve in the third year of the Test group was 0.658.The model can be used as an independent predictor to e-valuate the prognosis of patients with gastric cancer.There were significant differences in the expression of naive B cells,activated memory CD4+T cells,resting mast cells and activated mast cells between the high and low risk groups.The expression of PD-L1 in the low-risk group was increased,and the effect of anti-PD-1 treatment was more significant.Conclusion This study identified an immune-related genes prognostic risk model for gastric cancer,which can effectively evaluate the prognosis of gastric cancer patients and guide individualized treatment.