Authors
Zhouyu Ning, Ying Zhu, Hui Li, Zhiqiang Meng
Published in
Translational oncology. Volume 73. Pages 103025. Sep 06, 2026. Epub Sep 06, 2026.
Abstract
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.
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.
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.
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.
PMID:
42702145
Bibliographic data and abstract were imported from PubMed on 07 Sep 2026.
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