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