Abstract
<title>Abstract</title> <p>Background Metabolic reprogramming contributes to gastric cancer progression and immune regulation. Copper and lipid metabolism are interconnected, but their combined prognostic value in gastric cancer remains unclear. This study aimed to develop and validate a copper- and lipid-metabolism-related gene signature and explore its biological relevance. Methods Transcriptomic and clinical data were obtained from TCGA-STAD and GSE66229. Differentially expressed copper- and lipid-metabolism-related genes were identified, followed by consensus clustering, Cox regression and LASSO Cox analysis to construct a prognostic model. The signature was externally validated, and its associations with clinical characteristics, immune infiltration, tumor mutational burden, immunotherapy-related indicators, drug sensitivity and single-cell transcriptomic features were evaluated. In vitro assays examined the function of GAD1. Results A total of 217 copper- and lipid-metabolism-related differentially expressed genes were identified. Consensus clustering classified gastric cancer into six metabolic subtypes with distinct survival outcomes and tumor microenvironmental features. A 15-gene prognostic signature was established and showed stable predictive performance in the TCGA and GSE66229 cohorts. High-risk patients had poorer overall survival and were characterized by stromal activation, extracellular matrix remodeling and an immunosuppressive microenvironment. Low-risk tumors showed higher tumor mutational burden and enrichment of DNA repair and metabolic homeostasis pathways. Single-cell analysis indicated that GAD1-positive cells had enhanced copper-lipid metabolic activity and elevated epithelial-mesenchymal transition, hypoxia and fatty acid metabolism scores. GAD1 knockdown suppressed gastric cancer cell proliferation, colony formation and migration. Conclusion This study established a copper-lipid metabolism-related prognostic signature for gastric cancer, providing a potential framework for risk stratification and biomarker development.</p>