Abstract
Amino acid metabolic (AAM) reprogramming is a key characteristic of gastric cancer (GC) cells metabolic remodeling, which regulates cell growth, survival, immune cell activation and function to affect tumor immune escape. This study aims to systematically investigate AAM reprogramming in gastric cancer (GC) and construct prognostic model, and validate gene signatures for predictive value and clinical decision-making. This study leveraged data from TCGA and GEO to construct a prognostic model related to AAM and assess its clinical relevance in GC. We identified differentially expressed genes and conducted GO, GSEA, and GSVA enrichment analyses, along with constructing PPI networks and interaction networks of mRNA-miRNA, mRNA-TF, and mRNA-RBP. Additionally, immune infiltration analysis was performed and the relationships between eight hub-type amino acid metabolism-related genes (AAMRGs) and immune cells was investigated using scRNA-seq datasets. Lastly, we validated the elevated expression of these eight genes in GC cells through PCR. The study constructed a prognostic model for GC based on AAMRGs, identifying 16 key genes: ACLY, ADH4, COL1A1, F2, GADL1, GAMT, HBB, KYNU, MRI1, MTHFR, NR1D1, PDK4, SLC1A7, SLC25A15, SLC52A3, and SYCE2. Statistical analysis showed that 14 of these genes showed significant differential expression between tumor and normal tissues. Furthermore, the model demonstrated strong correlations with OS outcomes. Immune infiltration analysis indicated that various immune cell types were significantly associated with the expression of 8 hub genes, highlighting their potential role in the tumor microenvironment and immune response modulation. Furthermore, elevated expression of these genes in GC cells was validated through PCR, highlighting their relevance as potential biomarkers and therapeutic targets. Our AAMRGs prognostic model reveals AAMRGs as independent prognostic factors for GC, highlighting their association with prognosis and immune cell infiltration. These findings provide important insights for improving survival outcomes and advancing immunotherapy strategies in GC.