Enhancing breast cancer outcomes with machine learning-driven glutamine metabolic reprogramming signature.

ABSTRACT:

Background

This study aims to identify precise biomarkers for breast cancer to improve patient outcomes, addressing the limitations of traditional staging in predicting treatment responses.

Methods

Our analysis encompassed data from over 7,000 breast cancer patients across 14 datasets, which included in-house clinical data and single-cell data from 8 patients (totaling 43,766 cells). We utilized an integrative approach, applying 10 machine learning algorithms in 54 unique combinations to analyze 100 existing breast cancer signatures. Immunohistochemistry assays were performed for empirical validation. The study also investigated potential immunotherapies and chemotherapies.

Results

Our research identified five consistent glutamine metabolic reprogramming (GMR)-related

SUBMITTER: Li X 

PROVIDER: S-EPMC11097668 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature