Development and Validation of a Cuproptosis-Based Risk Score Model for Predicting Neoadjuvant Chemotherapy Response...

Area of Science:

• Transcriptomics
• Cancer Biology
• Immunology

Background:

• Neoadjuvant chemotherapy (NAC) response varies significantly in breast cancer (BC).
• Identifying predictive biomarkers for NAC is crucial for personalized treatment.
• Cuproptosis, a newly identified form of regulated cell death, is implicated in cancer progression.

Purpose of the Study:

• To develop and validate a transcriptomic risk score model based on cuproptosis-related genes for predicting NAC response in BC.
• To explore the association between this risk score and the tumor immune microenvironment.
• To investigate the role of key cuproptosis-related genes in BC and chemotherapy sensitivity.

Main Methods:

• Transcriptomic and clinical data from TCGA and GEO databases were analyzed.
• A 10-gene risk score model was constructed using LASSO-based Cox regression.
• Model performance was assessed using survival analysis, ROC curves, GSEA/GSVA, and external validation. Drug sensitivity and gene expression were also analyzed.

Main Results:

• A 10-gene cuproptosis-related risk score model was successfully developed.
• The risk score demonstrated good predictive performance for NAC response in both training and testing cohorts (AUCs 0.719 and 0.689, respectively).
• The model correlated with survival, BC subtypes, tumor stage, and pathologic response, with key genes linked to NAC response.

Conclusions:

• The developed cuproptosis-based risk score is a promising tool for predicting NAC response in breast cancer patients.
• This model may facilitate personalized treatment decisions and offers insights into the role of cuproptosis in immune modulation and chemotherapy sensitivity.