Prediction of lung metastasis in breast cancer patients using machine learning classifiers

Breast cancer is the most common cancer among women, and metastasis to the lung is associated with poor prognosis. Reliable biomarkers for predicting lung metastasis are urgently needed to improve early detection and clinical decision-making. This study utilized microarray datasets comprising gene expression profiles and clinical data from primary breast cancer patients who were followed for lung metastasis outcomes. High-throughput screening, combined with Venn diagram analysis, was used to identify common candidate probes, and the least absolute shrinkage and selection operator (LASSO) method selected eleven genes for model development.