Yu Shanshan, Hu Chuan, Cai Luya, Du Xuedan, Lin Fan, Yu Qiongjie, Liu Lixiao, Zhang Cheng, Liu Xuan, Li Wenfeng, Zhan Yu
Department of Chemoradiation Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Department of Orthopaedic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
Front Oncol. 2020 Sep 18;10:1778. doi: 10.3389/fonc.2020.01778. eCollection 2020.
Gastric cancer (GC) is one of the most common malignancies worldwide, exhibiting a high morbidity, and mortality. As the various treatment methods for gastric cancer are limited by disadvantages, many efforts to improve the efficacy of these treatments are being taken. Metabolic recombination is an important characteristic of cancer and has gradually caused a recent upsurge in research. However, systematic analysis of the interaction between glycolysis and GC patient prognosis and its potential associations with immune infiltration is lacking but urgently needed. We obtained the gene expression data and clinical materials of GC derived from The Cancer Genome Atlas (TCGA) dataset. Univariate and multivariate Cox proportional regression analyses were performed to select the optimal prognosis-related genes for subsequent modeling. We then validated our data in the GEO database and further verified the gene expression using the Oncomine database and PCR experiments. Besides, Gene set variation analysis (GSVA) analysis was employed to further explore the differences in activation status of biological pathways between the high and low risk groups. Furthermore, a nomogram was adopted to predict the individualized survival rate of GC patients. Finally, a violin plot and a TIMMER analysis were performed to analyse the characteristics of immune infiltration in the microenvironment. A seven-gene signature, including STC1, CLDN9, EFNA3, ZBTB7A, NT5E, NUP50, and CXCR4, was established. Based on this seven-gene signature, the patients in the training set and testing sets could be divided into high-risk and low-risk groups. In addition, a nomogram based on risk and age showed good calibration and moderate discrimination. The results proved that the seven-gene signature had a strong capacity to predict the GC patient prognosis. Collectively, the violin plot and TIMMER analysis demonstrated that an immunosuppressive tumor microenvironment caused by hyperglycolysis led to poor prognosis. Taken together, these results established a genetic signature for gastric cancer based on glycolysis, which has reference significance for the in-depth study of the metabolic mechanism of gastric cancer and the exploration of new clinical treatment strategies.
胃癌(GC)是全球最常见的恶性肿瘤之一,发病率和死亡率都很高。由于胃癌的各种治疗方法存在局限性,人们正在努力提高这些治疗方法的疗效。代谢重编程是癌症的一个重要特征,最近逐渐引起了研究热潮。然而,目前缺乏对糖酵解与GC患者预后之间相互作用及其与免疫浸润潜在关联的系统分析,但这一分析迫切需要。我们从癌症基因组图谱(TCGA)数据集中获取了GC的基因表达数据和临床资料。进行单变量和多变量Cox比例回归分析,以选择用于后续建模的最佳预后相关基因。然后我们在GEO数据库中验证了数据,并使用Oncomine数据库和PCR实验进一步验证了基因表达。此外,采用基因集变异分析(GSVA)分析进一步探究高风险组和低风险组之间生物通路激活状态的差异。此外,采用列线图预测GC患者的个体化生存率。最后,进行小提琴图和TIMMER分析,以分析微环境中免疫浸润的特征。建立了一个包括STC1、CLDN9、EFNA3、ZBTB7A、NT5E、NUP50和CXCR4的七基因特征。基于这一七基因特征,训练集和测试集中的患者可分为高风险组和低风险组。此外,基于风险和年龄的列线图显示出良好的校准度和适度的区分度。结果证明,七基因特征具有很强的预测GC患者预后的能力。总体而言,小提琴图和TIMMER分析表明,高糖酵解导致的免疫抑制性肿瘤微环境导致预后不良。综上所述,这些结果建立了基于糖酵解的胃癌基因特征,对深入研究胃癌代谢机制和探索新的临床治疗策略具有参考意义。