Machine learning-optimized metabolic biomarker panel for precision screening of early-stage pancreatic cancer in new-onset diabetes
Our study establishes a foundational metabolic biomarker strategy for precision screening of early-stage PDAC in NOD populations. The dysregulated one-carbon metabolites provide novel mechanistic insights into PDAC pathogenesis and offer actionable targets for clinical assay development. Future validation in multi-center cohorts is warranted to confirm clinical utility.
Authors
- Zihao Guo (ORCID: https://orcid.org/0000-0001-9002-0483)
- Rong Mu (ORCID: https://orcid.org/0000-0001-7332-4135)
- Lijuan Yang (ORCID: https://orcid.org/0000-0003-0429-036X)
- Weiliang Jiang (ORCID: https://orcid.org/0000-0002-0304-0847)
- Zhiyuan Cheng
- Haoran Sun (ORCID: https://orcid.org/0000-0001-6488-6531)
- Yu Guo (ORCID: https://orcid.org/0009-0001-4147-5315)
- Dongyan Wang (ORCID: https://orcid.org/0000-0001-5273-1145)
Institutions
- University of Shanghai for Science and Technology (CN)
- Second Military Medical University (CN)
- Shanghai Jiao Tong University (CN)
- Shanghai Pudong New Area Gongli Hospital (CN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-12-08
- DOI
- https://doi.org/10.3389/fendo.2025.1684608
- Primary Topic
- Pancreatic and Hepatic Oncology Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Foundation of Shanghai
- Shanghai Municipal Health Commission
- National Natural Science Foundation of China
- Shanghai Pudong New Area Health Commission