Integrative machine learning model for overall survival prediction in breast cancer using clinical and transcriptomic data

Abstract

Breast cancer is the most common malignancy in women, with the Luminal A subtype generally associated with favorable survival. However, age and menopausal status may influence tumor biology and prognosis. To improve prediction beyond conventional models, we analyzed transcriptomic and clinical data from the METABRIC cohort. Patients with Luminal A breast cancer were stratified into premenopausal, postmenopausal-nongeriatric, and geriatric (>= 70 years) groups. Differentially expressed genes (DEGs) were identified, and Boruta feature selection revealed 27 clinical and genomic variables. Random Forest, Logistic Regression, Multilayer Perceptron, and ensemble XGBoost models were trained with stratified 5-fold cross-validation, using SMOTE to correct class imbalance. Principal component analysis showed distinct clustering across age groups, while DEG analysis revealed 41 genes associated with age and survival. Key predictors included clinical variables (age, tumor size, NPI, radiotherapy) and molecular markers (ATM, HERC2, AKT2, FOXO3, CYP3A43). Among ML models, XGBoost demonstrated the highest performance (accuracy 98%, sensitivity 98%, specificity 97%, F1-score 0.99, AUC 0.86), outperforming other algorithms. These findings indicate that age-related transcriptomic changes impact survival in Luminal A breast cancer and that an ML-based integrative approach combining clinical and molecular variables provides superior prognostic accuracy, supporting its potential for clinical application.