| Title: | Machine learning-based clinical prediction model and multi-omics integration for assessing pancreatic cancer risk in new-onset diabetes |
| Journal: | Journal of Translational Medicine |
| Published: | 30 Jan 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41618402/ |
| DOI: | https://doi.org/10.1186/s12967-026-07767-1 |
Publication 18417
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Abstract
BackgroundGiven that pancreatic cancer (PC) is typically diagnosed at an advanced stage but is often preceded by new-onset diabetes mellitus (NODM), providing a window for early detection, we sought to develop and validate an interpretable machine-learning model integrated with multi-omics profiling to identify early biomarkers of NODM-associated PC.MethodsIn a population-based cohort, individuals with NODM-associated PC and NODM without PC were identified and randomly divided (70:30) into training and validation sets after feature selection. Eight machine learning (ML) classifiers were compared using fivefold cross-validation, and model performance was evaluated in terms of discrimination, calibration, and decision curve-based clinical utility. We evaluated interpretability using the Shapley additive explanations (SHAP) analyses. Mechanistically, Olink proteomic profiling and metabolomics were analyzed through clinical classifications and model-defined risk strata.ResultsCategorical boosting achieved the best performance in the independent validation set (AUROC = 0.844). The NODM cohort was stratified into high- (n = 2,362) and low-risk (n = 5,030) groups, and internal validation together with SHAP analyses demonstrated consistent model performance and identified clinically interpretable predictors. Proteomic and metabolomic analyses under clinical and risk-based grouping identified 39 overlapping differentially expressed proteins and 145 overlapping metabolites with enriched across 11 shared KEGG pathways. Cross-platform validation highlighted PLTP, CRTAC1, and ITGAV as serum biomarkers with a strong potential for early NODM-PC detection.ConclusionsWe developed an interpretable ML framework centered on NODM enables practical risk stratification for early PC detection by multi-omics and provides a pathway of ML-based triage followed by biomarker confirmation for earlier detection and diagnosis.</p>
14 Authors
- Jiabin Yang
- Bingxin Cao
- Anwaier Yuemaierabola
- Yanyu Gong
- Yusheng Guo
- Haoming Zhong
- Kailu Zhang
- Sichen Wang
- Qiaozhi Huang
- Jiaqing Li
- Tao Ye
- Jianwei Luo
- Yu Zhou
- Rufu Chen
1 Application
| Application ID | Title |
|---|---|
| 85224 | Identification of risk factors and construction of early-diagnosis model for predicting the risk of pancreatic cancer in new-onset diabetes population based on UK Biobank data |
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