Introduction
Breast cancer (BC) is among the most common malignancies worldwide, with metastasis being the main cause of cancer-related death. Accurate prediction of distant metastasis risk is crucial for improving patient outcomes. Based on the literature, we selected a panel of actin cytoskeleton-related genes as potential biomarkers. This study aimed to develop machine learning models predicting distant metastases in primary BC using their expression profiles.

Methods
We analyzed TCGA Breast Invasive Carcinoma data (Firehose Legacy) via cBioPortal. The following genes were studied: TAGLN, ANXA6, ANXA2, ECM1, PFN1, MYH9, CFL1, EZR, ACTN4, GSN, and FSCN1. Expression was normalized as log2(TPM+1). Metastasis status was defined by AJCC staging (M0: absent; M1: present). Due to class imbalance (902 M0 vs. 22 M1), SMOTE was applied. Data were split 80/20 into training/test sets with per-gene standardization. We tested k-NN, logistic regression, decision trees, random forest, and XGBoost. Hyperparameters were tuned with Grid Search and Optuna (10-fold CV). Models were evaluated by Accuracy, F1 Score, Precision, Sensitivity, and Specificity. Gene importance was assessed.

Results
All models except logistic regression (Accuracy = 0.65) performed well (Accuracy ≥ 0.89). Random forest achieved the best results (Accuracy = 0.983, Kappa = 0.967), followed by XGBoost (Accuracy = 0.961, Kappa = 0.922). CFL1, ANXA2, and MYH9 were the top predictors. TAGLN, FSCN1, and ECM1 showed higher importance in XGBoost, aligning with prior cell line studies.

Conclusions
Expression of cytoskeleton-related genes demonstrates strong potential for predicting distant metastasis in BC and warrants further validation in other cancer types.