ArticleJournal of gastroenterology2025
An integrative multi-omics analysis reveals a multi-analyte signature of pancreatic ductal adenocarcinoma in serum.
Article in Journal of gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Salivary metabolites for pancreatic cancer diagnosis: a systematic review and meta-analysis.BMC gastroenterology · 2025Pooled it
- Propionyl-CoA catabolism is a metabolic gatekeeper for fatty acid oxidation in pancreatic cancer.Oncogene · 2026Article
- Detection of Pancreatic Cancer via Specific Metabolite Markers: A Metabolomics Approach.Health science reports · 2026Article
- Metabolic Syndrome and Pancreatic Cancer: Linking Insulin Resistance, Inflammation, and Carcinogenesis.Diagnostics (Basel, Switzerland) · 2026Review
- Metabolic signatures and diagnostic models of ischemic stroke and its hypertensive subtype: a non-targeted metabolomics and machine learning.Frontiers in genetics · 2026Article
- Malignant Transformed and Non-Transformed Oral Leukoplakias Are Metabolically Different.International journal of molecular sciences · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundPancreatic ductal adenocarcinoma (PDAC) remains a formidable health challenge due to its detection at a late stage and a lack of reliable biomarkers for early detection. Although levels of carbohydrate antigen 19-9 are often used in conjunction with imaging-based tests to aid in the diagnosis of PDAC, there is still a need for more sensitive and specific biomarkers for early detection of PDAC.
methodsWe obtained serum samples from 88 subjects (patients with PDAC (n = 58) and controls (n = 30)). We carried out a multi-omics analysis to measure cytokines and related proteins using proximity extension technology and lipidomics and metabolomics using tandem mass spectrometry. Statistical analysis was carried out to find molecular alterations in patients with PDAC and a machine learning model was used to derive a molecular signature of PDAC.
resultsWe quantified 1,462 circulatory proteins along with 873 lipids and 1,001 metabolites. A total of 505 proteins, 186 metabolites and 33 lipids including bone marrow stromal antigen 2 (BST2), keratin 18 (KRT18), and cholesteryl ester(20:5) were found to be significantly altered in patients. We identified different levels of sphingosine, sphinganine, urobilinogen and lactose indicating that glycosphingolipid and galactose metabolisms were significantly altered in patients compared to controls. In addition, elevated levels of diacylglycerols and decreased cholesteryl esters were observed in patients. Using a machine learning model, we identified a signature of 38 biomarkers for PDAC, composed of 21 proteins, 4 lipids, and 13 metabolites.
conclusionsOverall, this study identified several proteins, metabolites and lipids involved in various pathways including cholesterol and lipid metabolism to be changing in patients. In addition, we discovered a multi-analyte signature that could be further tested for detection of PDAC.
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