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

Zhouyu Ning, Ying Zhu, Hui Li, Zhiqiang Meng

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Article in Translational oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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Zhouyu NingDepartment 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.

Ying ZhuDepartment 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.

Hui LiDepartment 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.

Zhiqiang MengDepartment 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. Electronic address: mengshca@fudan.edu.cn.

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6 · The paper itself

Abstract

backgroundPancreatic 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.

methodsWe 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.

resultsA 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.

conclusionsSerum 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.

Indexed as

BiomarkersDifferential diagnosisMetabolomicsPancreatic ductal adenocarcinomaPrognosis

Identifiers

PMID42702145

DOI10.1016/j.tranon.2026.103025

PMCPMC13572741

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