Identification of a novel prognostic model for gastric cancer utilizing glutamine-related genes.

Background: Glutamine metabolism presents a promising avenue for cancer prevention and treatment, but the underlying mechanisms in gastric cancer (GC) progression remain elusive. Methods: The TCGA-STAD and GEO GSE62254 datasets, containing gene expression, clinical information, and survival outcomes of GC, were meticulously examined. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to excavate a key module (MEturquoise), which was used to intersect with glutamine metabolism-related genes (GMRGs) and differentially expressed genes (DEGs) to identify differentially expressed GMRGs (DE-GMRGs). LASSO and Cox Univariate analyses were implemented to determine risk model genes. Correlation of the risk model with clinical parameters, pathways, and tumor immune microenvironments, was analyzed, and its prognostic independence was validated by Cox analyses. Finally, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed to validate the expression levels of MYB, LRFN4, LMNB2, and SLC1A5 in GC and para-carcinoma tissue. Results: The excavation of 4521 DEGs led to the discovery of the key MEturquoise module, which exhibited robust correlations with GC traits. The intersection analysis identified 42 DE-GMRGs, among which six genes showed consistency. Further LASSO analysis established Conclusion: The present study has generated a novel risk module containing four DE-GMRGs for predicting the prognosis and the response to immune checkpoint blockade treatments for GC. This risk model provides new insights into the involvement of glutamine metabolism in GC, warranting further investigation.