Pharmacogenomic characterization of a uric acid metabolism-related signature associated with prognosis and drug sensitivity in gastric cancer.

Gastric cancer (GC) remains a major cause of cancer-related mortality worldwide. Increasing evidence suggests that metabolic reprogramming contributes to GC progression, yet the prognostic significance of uric acid metabolism-related genes in GC remains unclear. This study aimed to characterize uric acid metabolism-related genes with prognostic and pharmacological relevance in gastric cancer and to explore their potential value for risk stratification and drug-response prediction. Differentially expressed genes between tumor and normal tissues were obtained from public databases and intersected with uric acid metabolism-related genes to identify candidate genes. Prognostic genes were screened using univariate Cox regression and machine learning algorithms, and a prognostic risk model was subsequently established. Patients were classified into high- and low-risk groups based on the risk score. Functional enrichment, somatic mutation, drug sensitivity, and single-cell transcriptomic analyses were further performed. RT-qPCR was used to validate gene expression in gastric cancer cell lines. A total of 657 candidate genes were identified from the intersection of 4514 differentially expressed genes and 3806 uric acid metabolism-related genes. Three genes, ABCG4, SERPINE1, and GPX3, were selected to construct the prognostic model. Patients in the high-risk group had significantly worse survival than those in the low-risk group. Multivariate Cox analysis showed that both risk score and age were independent prognostic factors for GC. Enrichment analysis indicated that pentose and glucuronate interconversions were significantly associated with the high-risk group. Drug sensitivity prediction showed lower predicted half-maximal inhibitory concentration (IC50) values for selected agents, including AZD8055, in the high-risk subgroup, suggesting a potential association with pharmacogenomic drug-response patterns. Single-cell analysis highlighted fibroblasts as a key cell population. RT-qPCR confirmed downregulation of ABCG4 and GPX3 and upregulation of SERPINE1 in GC cell lines. These findings identified prognostic features associated with uric acid metabolism in gastric cancer and suggest that ABCG4, SERPINE1, and GPX3 may be involved in metabolic dysregulation, matrix remodeling, and predicted pharmacogenomics response patterns.