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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.

Siqi Sun,Yingying Sun,A. Kinsela,Yunyi Zhu,N. LaBranche,Shengtao Chen,T. Waite

Published 2026 in Environmental Science and Technology

ABSTRACT

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.

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