Prognostic Significance of Cuproptosis-Related Gene Signatures in Breast Cancer Based on Transcriptomic Data Analysis

Area of Science:

• Oncology
• Molecular Biology
• Bioinformatics

Background:

• Breast cancer (BRCA) poses a significant health threat with increasing mortality.
• Current prognostic biomarkers for BRCA are limited.
• Cuproptosis, a copper-induced cell death, is a newly identified process with an unknown role in BRCA.

Purpose of the Study:

• To investigate the role of cuproptosis in breast cancer.
• To develop and validate a cuproptosis-related gene (CRG) risk model for predicting BRCA prognosis.
• To assess the association of the CRG risk model with clinical outcomes and tumor microenvironment characteristics.

Main Methods:

• Utilized transcriptomic data from TCGA and GEO databases for BRCA patients.
• Constructed and validated a risk model based on four CRGs: NLRP3, LIPT1, PDHA1, and DLST.
• Performed comprehensive bioinformatics analyses, including survival prediction and immune infiltration assessment.

Main Results:

• A robust CRG risk model was developed and validated, dividing BRCA patients into two subtypes.
• The risk model demonstrated significant associations with clinical outcomes, immune infiltrates, and tumor mutation burden (TMB).
• A clinical nomogram based on the risk score showed strong performance in predicting overall survival (OS).

Conclusions:

• The CRG risk model serves as a valuable tool for stratifying BRCA subtypes.
• The cuproptosis-related signature is crucial for predicting prognosis in breast cancer patients.
• This risk model holds potential clinical significance for improving BRCA patient management.