Prediction of Metastatic Risk in Breast Cancer by the Expression of Mechanobiological Markers

Distant metastasis is the leading cause of breast cancer-related mortality, yet its prediction from primary tumor profiles remains challenging.Cytoskeletal remodeling and cell motility are central to metastatic dissemination, suggesting mechanobiological genes as biologically relevant biomarkers.Here, we evaluated the ability of supervised machine learning models to distinguish metastatic from non-metastatic breast cancer samples using expression profiles of 11 actin cytoskeleton-related genes from the TCGA cohort.Ensemble models, particularly Random Forest and XGBoost, demonstrated strong discriminative ability and consistently outperformed other approaches after employing SMOTE for class balancing in exploratory analyses.CFL1, ANXA2, and MYH9 consistently emerged as the most informative predictors, highlighting mechanobiological processes as key drivers of metastatic risk.