Metabolic Detection of Pancreatic Ductal Adenocarcinoma through Machine Learning and Lipidomics
| Main Authors: | Guangxi Wang, Hantao Yao |
|---|---|
| Format: | info software Journal |
| Terbitan: |
, 2021
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/5148069 |
Daftar Isi:
- Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers characterized with rapid progression, metastasis and difficulty in diagnosis. However, there are no effective liquid-based testing methods available for PDAC detection. Here we introduce a minimally invasive approach that employs machine learning and lipidomics to establish a metabolic method to detect PDAC. Through greedy algorithm and mass spectrum feature selection, we optimized 17 characteristic metabolites as detection features and developed a LC-MS-based targeted assay. In this study, 1033 patients with PDAC at various stages were examined. This approach has achieved 86.74% accuracy with an AUC of 0.9351 in the large independent external validation cohort, and 85.00% accuracy with 0.9389 AUC in the prospective clinical cohort. Accordingly, single-cell sequencing, proteomics and mass spectrometry imaging were applied and revealed significant alterations of selected lipids in PDAC tissues. We propose that the ML-aided lipidomics approach is used for early detection of PDAC.