Abstract:Background and Aims Pancreatic cancer is a highly malignant digestive system tumor characterized by poor prognosis and limited therapeutic response. The tumor microenvironment plays a crucial role in its progression, where oxidative stress and lactate metabolism are two tightly interconnected processes influencing tumor growth, immune escape, and therapeutic resistance. However, their combined prognostic impact remains poorly understood. This study aimed to integrate oxidative stress– and lactate metabolism-related genes to establish and validate a robust prognostic model for pancreatic cancer, and to explore its association with immune microenvironment characteristics.Methods Transcriptomic and clinical data of 177 pancreatic cancer patients were obtained from TCGA database and an external validation was performed using the GEO dataset (GSE57495). Differentially expressed genes associated with oxidative stress and lactate metabolism were identified using the "limma" package. Univariate Cox regression was used to screen prognostic genes, followed by LASSO regression to construct a multi-gene risk model. Model performance was evaluated by Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, concordance index (C-index), nomogram calibration, and decision curve analysis (DCA). The CIBERSORT and ssGSEA algorithms were used to analyze immune cell infiltration and immune functional differences between risk groups.Results A six-gene signature (MUC1, KRT18, SDC1, AREG, DDC, and ATPAF2) was identified to construct the prognostic model. Based on the calculated risk score, patients were stratified into high- and low-risk groups. Kaplan-Meier analysis revealed significantly worse overall survival in the high-risk group (P<0.01). The model showed good predictive accuracy with 1-, 2-, and 3-year AUCs of 0.710, 0.674, and 0.649, respectively. The C-index and calibration curves confirmed its reliability, and multivariate Cox regression indicated that the risk score was an independent prognostic factor. External validation using GEO data demonstrated consistent predictive performance. Immune infiltration analysis revealed that M0 macrophages were markedly enriched in the high-risk group, while cytotoxic and effector T-cell populations were reduced, suggesting that an immunosuppressive microenvironment may contribute to poor outcomes.Conclusion This study developed and validated a novel prognostic model for pancreatic cancer based on oxidative stress and lactate metabolism-related genes. The model accurately predicts patient survival, reflects immune microenvironment heterogeneity, and provides new molecular insights for risk stratification and individualized therapeutic strategies in pancreatic cancer management.