Leveraging ML for profiling lipidomic alterations in breast cancer tissues: a methodological perspective.

ABSTRACT: In this study, a comprehensive methodology combining machine learning and statistical analysis was employed to investigate alterations in the metabolite profiles, including lipids, of breast cancer tissues and their subtypes. By integrating biological and machine learning feature selection techniques, along with univariate and multivariate analyses, a notable lipid signature was identified in breast cancer tissues. The results revealed elevated levels of saturated and monounsaturated phospholipids in breast cancer tissues, consistent with external validation findings. Additionally, lipidomics analysis in both the original and validation datasets indicated lower levels of most triacylglycerols compared to non-cancerous tissues, suggesting potential alterations in lipid storage and metabolis

SUBMITTER: Shahnazari P 

PROVIDER: S-EPMC11519355 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature