Xiong Zhijuan, Xing Chutian, Zhang Ping, Diao Yunlian, Guang Chenxi, Ying Ying, Zhang Wei
Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China.
Jiangxi Medical Center for Major Public Health Events, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China.
Biomedicines. 2023 Mar 22;11(3):983. doi: 10.3390/biomedicines11030983.
Gastric cancer (GC) is the third leading cause of cancer-related deaths worldwide. However, there are still no reliable biomarkers for the prognosis of this disease. This study aims to construct a robust protein-based prognostic prediction model for GC patients. The protein expression data and clinical information of GC patients were downloaded from the TCPA and TCGA databases, and the expressions of 218 proteins in 352 GC patients were analyzed using bioinformatics methods. Additionally, Kaplan-Meier (KM) survival analysis and univariate and multivariate Cox regression analysis were applied to screen the prognosis-related proteins for establishing the prognostic prediction risk model. Finally, five proteins, including NDRG1_pT346, SYK, P90RSK, TIGAR, and XBP1, were related to the risk prognosis of gastric cancer and were selected for model construction. Furthermore, a significant trend toward worse survival was found in the high-risk group ( = 1.495 × 10-7). The time-dependent ROC analysis indicated that the model had better specificity and sensitivity compared to the clinical features at 1, 2, and 3 years (AUC = 0.685, 0.673, and 0.665, respectively). Notably, the independent prognostic analysis results revealed that the model was an independent prognostic factor for GC patients. In conclusion, the robust protein-based model based on five proteins was established, and its potential benefits in the prognostic prediction of GC patients were demonstrated.
胃癌（GC）是全球癌症相关死亡的第三大主要原因。然而，对于这种疾病的预后，仍然没有可靠的生物标志物。本研究旨在为GC患者构建一个强大的基于蛋白质的预后预测模型。从TCPA和TCGA数据库下载GC患者的蛋白质表达数据和临床信息，并使用生物信息学方法分析352例GC患者中218种蛋白质的表达。此外，应用Kaplan-Meier（KM）生存分析以及单变量和多变量Cox回归分析来筛选与预后相关的蛋白质，以建立预后预测风险模型。最后，包括NDRG1_pT346、SYK、P90RSK、TIGAR和XBP1在内的五种蛋白质与胃癌的风险预后相关，并被选用于模型构建。此外，在高危组中发现了显著的生存恶化趋势（= 1.495 × 10-7）。时间依赖性ROC分析表明，与1年、2年和3年的临床特征相比，该模型具有更好的特异性和敏感性（AUC分别为0.685、0.673和0.665）。值得注意的是，独立预后分析结果显示该模型是GC患者的独立预后因素。总之，建立了基于五种蛋白质的强大蛋白质模型，并证明了其在GC患者预后预测中的潜在益处。
