Identification of Key Physicochemical Characteristics Which Influence Epithelial-to-Mesenchymal Transition of Lung Cells after Exposure to Engineered and Natural Stone Dusts via a Hybrid Machine Learning Approach

Exposure to respirable and inhalable dust from engineered stone is linked to lung diseases such as silicosis and COPD, yet the physicochemical properties affecting epithelial-to-mesenchymal transition (EMT) remain unclear. Here, 41 physicochemical properties were characterized across 30 dust samples and evaluated for associations with EMT progression in A549 lung epithelial cells after 24-h exposure. EMT was assessed using three hallmarks: E-cadherin downregulation, vimentin upregulation, and α-SMA upregulation. A hybrid feature selection strategy combining correlation filtering with LassoLarsCV reduced feature redundancy and improved model robustness. The selected features were modeled using optimized regressors (Extreme Gradient Boosting regressor for E-cadherin and Vimentin; Support Vector Machine for α-SMA), and SHAP analysis quantified each property's contribution. Crystalline silica emerged as the most influential factor, showing negative associations with E-cadherin and positive associations with Vimentin and α-SMA. In contrast, sodium-, aluminum-, and rutile-bearing components were associated with lower EMT progression, likely reflecting their occurrence within less reactive mineral phases than crystalline silica. Specific surface area and absolute ζ potential were positively associated with the EMT, indicating enhanced particle-cell interactions and surface-related signaling. These findings establish a framework for linking dust physicochemical characteristics to marker-specific EMT responses and demonstrate the effectiveness of interpretable machine learning for particulate toxicity assessment.