Prediction of Prognosis and Immunotherapy Response of Gastric Cancer Based on Glutamine Metabolism-related Genes

Background: 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. Methods: 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. Results: 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. Conclusion: 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.