Molecular subtyping and functional characterization of gastric cancer using arginine metabolism-related genes

Gastric cancer (GC) remains one of the most lethal malignancies worldwide due to its substantial heterogeneity, necessitating improved therapeutic strategies and prognostic tools. Recent studies have implicated dysregulated arginine metabolism in GC pathogenesis; however, its metabolic characteristics and clinical prognostic value are not fully understood.

GC samples were classified into three molecular subtypes based on the expression profiles of arginine metabolism-related genes (ArMGs). Prognostic ArMGs were identified within each subtype, and a cross-dataset prognostic model was developed to assess its predictive value. The associations of the model with anti-tumor immunotherapy, tumor immune microenvironment, signaling pathways, and gene expression patterns were further explored. Key candidate genes were validated using quantitative polymerase chain reaction (qPCR) in GC tissues and cell lines, and their biological functions were investigated through functional assays.

Consensus clustering of nine ArMGs stratified GC into three molecular subtypes: C1, C2, and C3. A prognostic prediction model for GC was constructed using differentially expressed genes among the subtypes and seven key prognostic genes: TMEM171, SLC5A1, DEGS2, MGP, C7, HMGCS2, and CREB3L3. The model demonstrated varying sensitivities to anti-tumor immunotherapy and showed strong correlations with immune-related tumor markers, the tumor immune microenvironment, and multiple signaling pathways. Among the ArMGs, ODC1 and ALDH18A1 were identified as critical contributors to GC. qPCR confirmed their elevated expression in GC tissues and cell lines. Silencing these genes significantly reduced GC cell proliferation, colony formation, and invasion.

This study comprehensively characterized the molecular features of ArMGs in GC and developed a robust, validated prognostic prediction model. The findings offer new molecular insights for predicting patient outcomes and guiding personalized therapeutic strategies in GC.