Zhu Xiaoling, Wang Jianfang, Jin Xueying, Chen Yiyi, Hu Liang, Zhao Jianguo
Department of Oncology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing 312000, China.
Department of Clinical Medicine, Wenzhou Medical University, Wenzhou 325035, China.
Mutat Res. 2022 Jul-Dec;825:111795. doi: 10.1016/j.mrfmmm.2022.111795. Epub 2022 Aug 12.
In this study, mRNA expression of gastric cancer tissue and clinical data of patients in TCGA-STAD dataset were used, together with the hypoxia-related gene sets in the MsigDB database, to screen hypoxia-related differentially expressed genes (DEGs) in GC. Thereafter, univariate and multivariate Cox regression analyses were carried out on hypoxia-related DEGs. The optimal feature genes related to prognosis were obtained to construct a prognostic risk assessment model. According to the model, the riskScore of GC patients was measured, and GC samples were assigned into high- and low-risk groups in accordance with the median riskScore. Based on the Kaplan-Meier curve and Receiver operating characteristic curve, validity of the prognostic risk assessment model was measured. Gene set enrichment analysis was performed on the two risk groups through Gene set enrichment analysis software. The results revealed that in the high-risk group, 9 signaling pathways were remarkably activated in several terms, like focal adhesion, extracellular matrix receptor interaction, Cell adhesion molecules cams, Cytokine-cytokine receptor interaction, TGF-beta signaling pathway, NOD-like receptor signaling pathway, JAK-STAT signaling pathway, Toll-like receptor signaling pathway and MAPK signaling pathway. In combination with riskScore and clinical factors, univariate and multivariate Cox regression analyses verified the independence of the model. Meanwhile, a nomogram was constructed to predict the 1-, 3- and 5-year survival of GC patients. The calibration curve indicated that the survival status predicted by the nomogram fitted better with actual survival status. On the whole, the prognostic risk model of GC on the basis of hypoxia-related genes demonstrated good predictive ability. It can provide more powerful technical support for clinicians to make prognostic determination and therapeutic plans.
在本研究中，使用了TCGA-STAD数据集中胃癌组织的mRNA表达和患者的临床数据，以及MsigDB数据库中的缺氧相关基因集，以筛选胃癌中与缺氧相关的差异表达基因（DEGs）。此后，对与缺氧相关的DEGs进行单变量和多变量Cox回归分析。获得与预后相关的最佳特征基因，以构建预后风险评估模型。根据该模型，测量胃癌患者的风险评分，并根据风险评分中位数将胃癌样本分为高风险组和低风险组。基于Kaplan-Meier曲线和受试者工作特征曲线，评估预后风险评估模型的有效性。通过基因集富集分析软件对两个风险组进行基因集富集分析。结果显示，在高风险组中，有9条信号通路在几个方面显著激活，如粘着斑、细胞外基质受体相互作用、细胞粘附分子、细胞因子-细胞因子受体相互作用、TGF-β信号通路、NOD样受体信号通路、JAK-STAT信号通路、Toll样受体信号通路和MAPK信号通路。结合风险评分和临床因素，单变量和多变量Cox回归分析验证了模型的独立性。同时，构建了列线图以预测胃癌患者1年、三年和5年生存率。校准曲线表明，列线图预测的生存状态与实际生存状态拟合较好。总体而言，基于缺氧相关基因的胃癌预后风险模型具有良好的预测能力。它可以为临床医生进行预后判断和制定治疗方案提供更有力的技术支持。
