Identification of copper related biomarkers in breast cancer using machine learning.

ABSTRACT:

Background

Breast cancer is the most prevalent and deadly cancer among women globally, necessitating more effective diagnostic and therapeutic approaches. This study aims to explore new treatment targets and diagnostic tools.

Methods

Employing machine learning techniques and utilizing PCR, IHC technologies, and multiple databases, we identified and validated genes closely linked with breast cancer and copper-induced cell death. We then explored how their expression levels impact cancer diagnosis, prognosis, immune cell infiltration, and drug sensitivity.

Results

This investigation identified three crucial genes-MT1M, GRHL2, and PKM-intimately associated with the copper death mechanism in breast cancer pathology. Validated through comprehensive analysis across cells, t

SUBMITTER: Wang J 

PROVIDER: S-EPMC12328876 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature