Using glucocorticoid receptor-related genes to create and validate a survival model predicting gastric cancer.

Gastric carcinoma remains a major health burden with poor response to standard chemotherapy and a median overall survival of approximately one year. Glucocorticoid receptors (GRs) play a dual role in cancer by modulating inflammation, immune responses, and tumor progression, yet their prognostic relevance in gastric cancer remains unclear. To identify robust prognostic markers, we explored the role of glucocorticoid receptor-related genes in disease progression and immune modulation. Gene expression and clinical data of 448 patients from TCGA were used as the training cohort, while GSE54129 (n = 132) and GSE62254 (n = 300) served for external validation. A total of 1591 related genes were retrieved from GeneCards. Multiple supervised learning algorithms were applied to rank candidate genes and construct survival models. Model performance was evaluated using the concordance index and time-dependent AUC across training and validation sets. The final GR-based prognostic model demonstrated stable predictive accuracy, and a nomogram integrating the risk score with clinical variables enabled individualized survival prediction. Risk groups defined by the model showed distinct differences in mutational profiles, immune microenvironment features, and predicted drug sensitivity. These findings highlight the prognostic relevance of GR signaling in gastric cancer and provide a clinically interpretable tool that may support personalized management.