Journal
SCIENCE ADVANCES
Volume 7, Issue 52, Pages -Publisher
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/sciadv.abh2724
Keywords
-
Categories
Funding
- National Key Research and Development Program of China [2016YFA0500302]
- National Natural Scientific Foundation of China [82030081, 81430056, 31420103905, 81874235, 30700349, 30440012]
- Beijing Municipal Science and Technology Commission [Z131100004013036]
- Shu Fan Education and Research Foundation
- Lam Chung Nin Foundation for Systems Biomedicine
Ask authors/readers for more resources
The study introduced a minimally invasive approach using machine learning and lipidomics to detect PDAC, achieving high accuracy rates through feature optimization and mass spectrum analysis. Single-cell sequencing, proteomics, and mass spectrometry imaging were applied to reveal lipid alterations in PDAC tissues, suggesting the potential for early detection of PDAC.
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, characterized by 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 uses machine learning (ML) and lipidomics to detect PDAC. Through greedy algorithm and mass spectrum feature selection, we optimized 17 characteristic metabolites as detection features and developed a liquid chromatography-mass spectrometry-based targeted assay. In this study, 1033 patients with PDAC at various stages were examined. This approach has achieved 86.74% accuracy with an area under curve (AUC) of 0.9351 in the large 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 notable alterations of selected lipids in PDAC tissues. We propose that the ML-aided lipidomics approach be used for early detection of PDAC.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available