Machine learning unveils key Redox signatures for enhanced breast Cancer therapy.

ABSTRACT:

Background

Breast cancer remains a leading cause of mortality among women worldwide, necessitating innovative prognostic models to enhance treatment strategies.

Methods

Our study retrospectively enrolled 9,439 breast cancer patients from 12 independent datasets and single-cell data from 12 patients (64,308 cells). Moverover, 30 in-house clinical cohort were collected for validation. We employed a comprehensive approach by combining ten distinct machine learning algorithms across 108 different combinations to scrutinize 88 pre-existing signatures of breast cancer. To affirm the efficacy of our developed model, immunohistochemistry assays were performed. Additionally, we investigated various potential immunotherapeutic and chemotherapeutic interventions.

Results

This st

SUBMITTER: Wang T 

PROVIDER: S-EPMC11549853 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature