基于癌症基因组图谱构建胃癌预后评估模型

目的 采用生物信息学方法,对癌症基因组图谱（TCGA）数据库中胃癌转录组数据进行分析,构建胃癌预后评估模型,筛选影响胃癌发生及预后的生物标志物。方法 从TCGA数据库下载胃癌转录组数据及临床病理资料（胃癌样本375例,癌旁正常样本32例）并合并成矩阵,采用R“edgeR”包筛选差异表达基因（DEGs）,采用R“Survival”包对DEGs进行Cox单因素、多因素回归分析,构建胃癌预后评估模型。结合临床病理特征,验证该模型在预后评估中的有效性。结果 基于“edgeR”包共筛选出4 332个DEGs,纳入Cox单因素分析,结果显示710个DEGs与胃癌预后相关（P＜0.01）,取 P＜0.001 的 25 个 DEGs 纳入 Cox 多因素分析,得到包含 8 个 DEGs（BCHE、INPP5J、VCAN、IGFBP1、CGB5、HP、PSG9、AFF2）胃癌预后评估模型,基于模型风险评分将样本分为高、低风险组,Kaplan-Meier生存曲线结果显示高、低风险组5年总生存率（OS）分别为56.20%、17.27%（P＜0.001）,ROC曲线证实该预测模型有一定的准确性（AUC=0.758）。将临床病理特征纳入Cox回归分析,结果显示,高龄和风险评估模型评分为高风险是影响TCGA胃癌患者预后的独立危险因素。结论 基于生物信息学方法构建的胃癌预后评估模型可成为胃癌预后判断的新指标。

Construction of prognostic predictive model of gastric cancer based on the cancer genome atlas

Objective To construct the prognostic evaluation model for gastric cancer (GC) and identify biomarkersrelated to the initiation and prognosis of GC through bioinformatics analysis of TCGA dataset. Methods The transcriptome data of GC and corresponding clinical information (containing 375 GC samples and 32 para-carcinoma samples) were downloaded from the TCGA website and merged into a matrix. The differential expressed genes (DEGs) were screened between GC and adjacent normal tissues using R“edgeR”package, and Cox univariate and multivariate regression analyses were performed by R“survival”package, and the predictive signature model of GC prognosis was established. Combined with clinicopathologic parameters, the role of the gene signature in predicting prognosis of GC was validated. Results A total of 4 332 genes were regarded as DEGs based on“edgeR”package. Next, Cox univariate regression analysis screened 710 DEGs associated with prognosis, among which 25 DEGs were incorporated into Cox multivariate analysis on the threshold of P value＜0.001. Consequently, we obtained an 8 DEG (BCHE, INPP5J, VCAN, IGFBP1, CGB5, HP, PSG9,AFF2) predictive model for GC prognosis. Based on the median value of risk score, GC samples were divided into high and low risk groups. Kaplan-Meier survival curve indicated that the 5-year survival rates in high and low risk groups were56.20% and 17.27%, respectively (P＜0.001). ROC curve confirmed the medium accuracy of the predictive model in GC prognosis. Furthermore, the 8 gene signature was proved to be an independent prognostic factor in GC whenclinicalpathologic information was incorporated into the Cox multivariate regression model. Conclusion Prognostic evaluation model of gastric cancer based on bioinformatics method can become new indicators for prognosis of gastric cancer.