園田 洋史

Background: Liquid biopsy using bodily fluids enables noninvasive acquisition of diverse tumor-derived molecules for comprehensive characterization of tumor profiles. Metabolomic analysis, in particular, may accurately reflect disease pathogenesis and holds promise for clinical diagnostic applications. Objective: This study explored metabolic alterations associated with pancreatic ductal adenocarcinoma (PDAC) using non-targeted metabolomic analysis of pancreatic juice to construct a preliminary diagnostic model based on selected metabolites. Methods: Pancreatic juice samples were collected intraoperatively and postoperatively from patients undergoing pancreaticoduodenectomy for PDAC (n = 11) and from those who had non-PDAC diseases, including benign conditions such as chronic pancreatitis and non-pancreatic malignancies such as distal bile duct adenocarcinoma and ampullary adenocarcinoma (n = 14). Non-targeted metabolomic analysis was performed using LC-QTOF-MS. Data were processed using MS-DIAL and MetaboAnalyst, and components showing intergroup differences were selected via PLS-DA. A diagnostic model was constructed using logistic regression based on annotated metabolites. Results: PLS-DA identified 56 discriminative components, of which 19 were successfully annotated. One metabolite was notably increased and 22 were relatively decreased in pancreatic juice of patients with PDAC. Among known metabolites that tended to decrease were isocitric acid, citric acid, and several oxidized fatty acids. A tentative logistic regression-based diagnostic model using these selected metabolites showed moderate discriminative performance. Citric acid was included in the final three-variable model, suggesting its potential as a candidate marker for PDAC discrimination. Conclusions: Pancreatic juice reflects PDAC-associated metabolic changes and may contain candidate diagnostic biomarkers. Metabolites annotated in this study may have potential as novel markers, and further studies on unknown components could help advance PDAC diagnosis and treatment.