A multi-stage prospective study evaluates serum metabolomic signatures for the differential diagnosis and prognostic stratification of PDAC.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) lacks biomarkers for accurate diagnosis and prognostic stratification. The standard, CA19-9, has suboptimal specificity in differentiating PDAC from mimics like chronic pancreatitis (CP). We investigated serum metabolomic signatures to address these challenges. METHODS: We conducted a prospective, multi-cohort (n = 221) untargeted metabolomics study, analyzing PDAC (n = 66), healthy control (n = 92), other cancer (n = 40), and CP (n = 23) groups. Machine learning and survival analysis were employed to build diagnostic and prognostic models from serum samples collected at diagnosis. RESULTS: A two-metabolite panel distinguished PDAC from other cancers (AUC=0.894), and a five-metabolite signature showed high discrimination between PDAC and CP (AUC=0.998; 95% CI, 0.993-1.000). A leakage-resistant nested cross-validation sensitivity analysis yielded a mean AUC of 0.955 (SD, 0.020). The exploratory 18-metabolite risk score separated high- and low-risk groups and remained associated with overall survival after adjustment for stage, age, sex, and CA19-9 (adjusted HR, 3.94; 95% CI, 2.05-7.58; P < 0.001), with a C-index of 0.601. CONCLUSIONS: Serum metabolomic profiles identified compact candidate panels that provided information complementary to CA19-9 for PDAC differential diagnosis, while the 18-metabolite risk score was associated with overall survival. These findings support targeted assay development and prospective multicenter evaluation of serum metabolomics for the clinical characterization of PDAC.