Zheng Guo-Liang, Zhang Guo-Jun, Zhao Yan, Zheng Zhi-Chao
Department of Gastric Surgery, Cancer Hospital of China Medical University (Liaoning Cancer Hospital and Institute), Shenyang, China.
Department of Pathophysiology, College of Basic Medicine Science, China Medical University, Shenyang, China.
Front Oncol. 2022 Apr 28;12:901182. doi: 10.3389/fonc.2022.901182. eCollection 2022.
The objective was to construct a prognostic risk model of stomach adenocarcinoma (STAD) based on The Cancer Proteome Atlas (TCPA) to search for prognostic biomarkers. Protein data and clinical data on STAD were downloaded from the TCGA database, and differential expressions of proteins between carcinoma and para-carcinoma tissues were screened using the R package. The STAD data were randomly divided into a training set and a testing set in a 1:1 ratio. Subsequently, a linear prognostic risk model of proteins was constructed using Cox regression analysis based on training set data. Based on the scores of the prognostic model, sampled patients were categorized into two groups: a high-risk group and a low-risk group. Using the testing set and the full sample, ROC curves and K-M survival analysis were conducted to measure the predictive power of the prognostic model. The target genes of proteins in the prognostic model were predicted and their biological functions were analyzed. A total of 34 differentially expressed proteins were screened (19 up-regulated, 15 down-regulated). Based on 176 cases in the training set, a prognostic model consisting of three proteins (COLLAGEN VI, CD20, TIGAR) was constructed, with moderate prediction accuracy (AUC=0.719). As shown by the Kaplan-Meier and survival status charts, the overall survival rate of the low-risk group was better than that of the high-risk group. Moreover, a total of 48 target proteins were identified to have predictive power, and the level of proteins in hsa05200 (Pathways in cancer) was the highest. According to the results of the Univariate and multivariate COX analysis, tri-protein was identified as an independent prognostic factor. Therefore, the tri-protein prognostic risk model can be used to predict the likelihood of STAD and guide clinical treatment.
目的是基于癌症蛋白质组图谱（TCPA）构建胃腺癌（STAD）的预后风险模型，以寻找预后生物标志物。从TCGA数据库下载STAD的蛋白质数据和临床数据，并使用R包筛选癌组织和癌旁组织之间蛋白质的差异表达。将STAD数据以1:1的比例随机分为训练集和测试集。随后，基于训练集数据使用Cox回归分析构建蛋白质的线性预后风险模型。根据预后模型的得分，将抽样患者分为两组：高风险组和低风险组。使用测试集和全样本进行ROC曲线和K-M生存分析，以评估预后模型的预测能力。预测预后模型中蛋白质的靶基因并分析其生物学功能。共筛选出34种差异表达蛋白质（19种上调，15种下调）。基于训练集中的176例病例，构建了由三种蛋白质（胶原蛋白VI、CD20、TIGAR）组成的预后模型，预测准确性中等（AUC=0.719）。 Kaplan-Meier和生存状态图显示，低风险组的总生存率优于高风险组。此外，共鉴定出48种具有预测能力的靶蛋白，其中hsa05200（癌症通路）中的蛋白质水平最高。根据单因素和多因素COX分析结果，三蛋白被确定为独立的预后因素。因此，三蛋白预后风险模型可用于预测STAD的可能性并指导临床治疗。
