Gong Saisai, Yang Sheng, Zhang Tianyi, Li Jie, Wan Xin, Fang Yifei, Liu Tong, Li Chengyun, Zhou Yun, Liang Geyu
Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, 210009, China.
School of Public Health, Lanzhou University, Lanzhou, Gansu, 730000, China.
Curr Med Chem. 2024 Aug 20. doi: 10.2174/0109298673297812240811182813.
Reprogramming of glutamine metabolism in Gastric Cancer (GC) can significantly affect the tumor immune microenvironment and immunotherapy. This study examines the role of glutamine metabolism in the microenvironment and prognosis of gastric cancer.
We obtained gene expression data and clinical information of patients from the TCGA database. The patients were divided into two metabolic subtypes based on consistent clustering. A prognostic risk model containing three glutamine metabolism-related genes (GMRGs) was developed using Lasso-Cox. It was validated by the GEO validation cohort. Additionally, the immune microenvironment composition of the highand low-risk groups was assessed using ESTIMATE, CIBERSORT, and ssGSEA. Drug sensitivity analysis was conducted using the "oncoPredict" R package.
We outlined the distinct clinical characteristics of two subtypes and developed a prognostic risk model. The high-risk group has a poorer prognosis due to an increased expression of immune checkpoints and immunosuppressive cellular infiltration. Our analysis, which included Cox risk regression, ROC curves, and nomogram, demonstrated that this risk model is an independent prognostic factor. The TIDE score was higher in the high-risk group than in the low-risk group. Additionally, the high-risk group did not respond well to chemotherapeutic drug treatment.
This study shows that modelling glutamine metabolism is a good predictor of prognosis and immunotherapy efficacy in gastric cancer. Thus, we can better understand the role of glutamine metabolism in the development of cancer and use these insights to develop more targeted and effective treatments.
胃癌（GC）中谷氨酰胺代谢的重编程可显著影响肿瘤免疫微环境和免疫治疗。本研究探讨谷氨酰胺代谢在胃癌微环境及预后中的作用。
我们从TCGA数据库获取患者的基因表达数据和临床信息。基于一致性聚类将患者分为两种代谢亚型。使用Lasso-Cox方法构建了一个包含三个谷氨酰胺代谢相关基因（GMRGs）的预后风险模型。通过GEO验证队列对其进行验证。此外，使用ESTIMATE、CIBERSORT和ssGSEA评估高风险组和低风险组的免疫微环境组成。使用“oncoPredict”R包进行药物敏感性分析。
我们概述了两种亚型的不同临床特征，并构建了一个预后风险模型。高风险组由于免疫检查点表达增加和免疫抑制细胞浸润，预后较差。我们包括Cox风险回归、ROC曲线和列线图在内的分析表明，该风险模型是一个独立的预后因素。高风险组的TIDE评分高于低风险组。此外，高风险组对化疗药物治疗反应不佳。
本研究表明，构建谷氨酰胺代谢模型是胃癌预后和免疫治疗疗效的良好预测指标。因此，我们可以更好地理解谷氨酰胺代谢在癌症发展中的作用，并利用这些见解开发更具针对性和有效性的治疗方法。
