Elucidating the Inhibitory Potential of Statins Against Oncogenic c-Met Tyrosine Kinase Through Computational and Cell-based Studies - Alizadeh, Elham Ahmad; Karami, Leila; Ghasemi, Fahimeh; Shadboorestan, Amir; Torabi, Mohammad Reza; Montazeri, Vahideh; Ostad, Seyed Nasser (Brieflands, 2025)
Background: The cellular mesenchymal-epithelial transition (c-Met) receptor, a member of the receptor tyrosine kinase family, is a novel therapeutic target for treating many cancers, including stomach cancer. Overexpression of c-Met and/or high levels of hepatocyte growth factor (HGF) correlate with poor prognosis. Statins, as LDL-lowering agents, are exploited to obtain anti-cancer effects via a wide range of pleiotropic effects. Objectives: The present study aimed to discover the most effective statin as a c-Met signaling inhibitor through computational and experimental approaches. Methods: Two main computational approaches, i.e., machine learning (ML) model and molecular dynamics (MDs) simulation, were followed by cytotoxicity, flow cytometric analysis, and western blot assay on AGS and MKN-45 gastric cancer cells. Results: The machine learning section was founded on developing tree-based classification algorithms to predict the biological activities of the proposed statin structures as c-Met receptor inhibitors. In the second step, molecular docking and MD simulation were utilized to estimate the biomolecular interactions. The proposed classification models reveal that all structures have more than 200 nM biological activities. Machine learning led the experiment to find fluvastatin and pitavastatin as the two compounds with the highest inhibitory effects. In cell-based assays, both tested statins exhibited cytotoxicity and induced apoptosis, accompanied by sub-Gi accumulation in gastric cancer cells. However, no significant reduction in c-Met phosphorylation was observed by western blot. Conclusions: No relation between the statins' inhibitory effect and the c-Met pathway on cancerous cells could be reported.
Keywords: Boosting Machine Learning Algorithms; Molecular Docking Simulation; Molecular Dynamics Simulation; Stomach Neoplasms; Proto-oncogene Proteins c-Met; Hydroxymethylglutaryl-CoA Reductase Inhibitors