A Tissue‐based Biomarker Risk Score for Predicting Survival in Pancreatic Ductal Adenocarcinoma

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Background/Aim: Pancreatic cancer is a highly aggressive disease, with limited prognostic tools available for risk stratification. This study aimed to evaluate the prognostic significance of nine tissue biomarkers and develop a biomarker-based risk score for predicting patient survival. Patients and Methods: Tumor samples from 141 resected patients with pancreatic cancer were analyzed with tissue microarrays and immunohistochemistry to assess the expression levels of CA 19-9, CA 50, CA 242, CA 724, GDF15, MMP7, MUC2, TFF1, and THBS2. A Lasso-Cox regression model was used to develop a prognostic risk score and the performance of the risk score was assessed using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves. Results: Among the nine biomarkers, CA19-9, CA50, CA242, CA724, and THBS2 were identified as significant predictors of survival in univariable analyses. A prognostic model was constructed and included CA19-9, CA724, THBS2, tumor location, resection margin status, grade, and American Joint Committee on Cancer stage. The prognostic risk score effectively stratified patients into high- and low-risk groups, demonstrating a significant difference in median survival (14.8 vs. 36.0 months) and 5-year survival (5.9% vs. 26.0%) (p<0.001). The model achieved good predictive performance for long-term survival with an AUC of 0.704. Conclusio: This study identifies several tissue biomarkers associated with survival and introduces an integrative risk model to stratify pancreatic cancer patients by outcomes. The model shows good discriminatory ability and may provide a basis for more personalized risk assessment and treatment planning, although additional validation is required.

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