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Published on: December 19, 2025
A multi-stage prospective study evaluates serum metabolomic signatures for the differential diagnosis and prognostic
Zhouyu Ning1, Ying Zhu1, Hui Li1
1Department of Minimally invasive therapy center, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China; Department of Integrative Oncology, Fudan University Shanghai Cancer Centre, Shanghai, China.
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.
