Construction of an Extracellular Matrix-related Gene Model for Predicting Prognosis and Immune Features in Gastric Cancer

Introduction: The extracellular matrix (ECM) critically shapes the gastric cancer (GC) microenvironment and influences tumor initiation, proliferation, invasion, and angiogenesis. However, the prognostic significance of ECM-related genes in GC has not been systematically elucidated. Methods: Differentially expressed genes (DEGs) between GC and adjacent normal tissues were extracted from The Cancer Genome Atlas. ECM-related DEGs were identified and incorporated into a prognostic model using LASSO Cox regression. Risk scores were calculated for each patient, and a nomogram integrating clinical variables was developed. Model performance was assessed by receiver operating characteristic (ROC) analysis and validated in two independent external cohorts. Results: The ECM-based risk model stratified GC patients into high- and low-risk groups with significantly different overall survival. ROC analysis demonstrated strong predictive accuracy, and the nomogram showed good concordance between predicted and observed outcomes. Importantly, patients in the high-risk group exhibited elevated expression of immune checkpoint molecules. Discussion: The ECM-related prognostic model provides a novel framework for risk assessment in GC. Beyond prognostication, the model highlights an immunological link: the high-risk group demonstrated upregulation of immune checkpoints. These findings suggest the model not only predicts survival but may also identify subgroups of patients more likely to benefit from immunotherapy. Conclusion: We established and validated a robust ECM-related prognostic model and nomogram for GC. This tool provides clinically relevant prognostic information and may inform personalized treatment strategies, particularly in identifying patients who could benefit from immunotherapy.