Development of a Prognostic Model for HR-positive HER2-negative and Node-negative Breast Cancer: Integrating Clinical and Transcriptional Biomarkers

Abstract

Purpose In this study, a prognostic model was constructed for HR-positive HER2-negative (HR+/HER2–) and node-negative breast cancer by integrating clinical and transcriptional biomarkers, with a particular focus on exploring both main effects and gene-gene (G × G) interactions. Methods Univariate and multivariate Cox regression were used to analyze three independent trans-ethnic cohorts with a total of 2180 samples. Independent prognostic factors were used to construct a prediction model. The Model was validated by ROC curves, calibration curve and decision curve analysis (DCA).The molecular basis of the Model was illustrated by integrating bulk-tumor and single-cell RNAseq datasets. Results Our findings revealed that a combination of clinical and transcriptional factors can improve the accuracy of prognostic models for HR+/HER2– and node-negative breast cancer. The Model achieved satisfactory discrimination, with the area under the curve (AUC) ranging from 0.65 (Metabric, 10-year survival) to 0.88 (GSE96058, 3-year survival). Conclusion This research provides a powerful tool for predicting outcomes in HR+/HER2– and node-negative breast cancer, offering initial insights into the molecular mechanisms that can guide future investigations.