Metabolomic profiling of pancreatic juice: a potential prognostic tool for pancreatic ductal adenocarcinoma

Background Treatment for pancreatic Ductal Adenocarcinoma (PDAC) remains a major challenge despite recent advancements. Selecting biological subgroups could improve stratification and address targeted therapies. Metabolomic analysis of pancreatic juice (PJ) is a promising technology to identify disease-specific profiles. The aim of this study is to evaluate the association of PJ metabolomics with PDAC patients’ clinical profiles, prognostic characteristics and long-term outcomes. Methods Data of patients undergoing pancreatic resection for PDAC at Humanitas Research Hospital were prospectively collected. PJ samples were retrieved intraoperatively and processed through flow injection-high resolution mass spectrometry analysis (FIA-HRMS). Untargeted and targeted analysis were performed. Univariable and multivariable models assessed associations between metabolites and clinicopathologic variables. Survival analyses evaluated correlations with long-term outcomes. Results Fifty-eight patients were included; metabolomic analysis of PJ revealed three metabolic clusters with comparable demographic characteristics. Cluster 2 showed higher levels of amino acids, phospholipids, sphingomyelin and cholesteryl esters compared to Cluster 3 . Cluster 2 showed higher likelihood of T3-T4 tumours (OR 10.68, CI 1.84-61.93, p = 0.01) and lymph nodal involvement (OR 14.02, CI 1.58-124.2, p = 0.02). Even though Kaplan-Meier curves did not show clear survival separation across clusters, higher cholesteryl ester concentrations were associated with improved DFS. Conclusion The correlation between metabolomic clusters and cancer staging, along with the association of specific metabolites with survival outcomes, suggest that PJ composition might reflect PDAC aggressiveness. The feasibility of preoperative PJ endoscopic collection underscores its possible clinical application for patients' stratification and potential identification of therapeutic targets. Larger cohorts’ analyses are needed to validate these findings.