Combining metabolomics and machine learning to discover biomarkers for early-stage breast cancer diagnosis.

ABSTRACT: There is an urgent need for better biomarkers for the detection of early-stage breast cancer. Utilizing untargeted metabolomics and lipidomics in conjunction with advanced data mining approaches for metabolism-centric biomarker discovery and validation may enhance the identification and validation of novel biomarkers for breast cancer screening. In this study, we employed a multimodal omics approach to identify and validate potential biomarkers capable of differentiating between patients with breast cancer and those with benign tumors. Our findings indicated that ether-linked phosphatidylcholine exhibited a significant difference between invasive ductal carcinoma and benign tumors, including cases with inconsistent mammography results. We observed alterations in numerous lipid species, inc

SUBMITTER: Anh NK 

PROVIDER: S-EPMC11493280 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature