Development and Validation of a Cuproptosis-Based Risk Score Model for Predicting Neoadjuvant Chemotherapy Response in Breast Cancer: A Transcriptomic Analysis
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Lihai Zhang1, Jiao Wang2, Baihong Tan1
1Department of General Surgery, The First Affiliated Hospital of Jiamusi University, Jiamusi, 154003, Heilongjiang, China, jmsuf1.cj68.com.
Objective:
This study developed a cuproptosis-related transcriptomic risk score model to predict neoadjuvant chemotherapy (NAC) response in breast cancer (BC) patients and explored its association with the tumor immune microenvironment.
Methods:
Analysis of transcriptomic and clinical data from TCGA and GEO revealed differentially expressed cuproptosis-related genes. LASSO-based Cox regression was used to build the risk score. Model performance was evaluated using Kaplan-Meier survival, ROC curves, and GSEA/GSVA in the training cohort and further validated in an independent external cohort. Drug sensitivity was predicted using the oncoPredict tool, and RT-qPCR was used to validate key gene expression.
Results:
A 10-gene prognostic model was developed based on the identification of 71 cuproptosis-related genes. The risk score correlated with survival outcomes, PAM50 subtypes, tumor stage, and pathologic response. It showed good predictive performance in both training (AUC = 0.719) and testing (AUC = 0.689) cohorts. Key genes (CIRBP, INPP4B, IL6ST, and CCL20) were validated and linked to NAC response.
Conclusion:
The cuproptosis-based risk score model effectively predicts NAC response and may guide personalized treatment in BC. It also reveals the relevance of cuproptosis-related genes in immune modulation and chemotherapy sensitivity.
