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Elucidating the Inhibitory Potential of Statins Against Oncogenic c-Met Tyrosine Kinase Through Computational and
Elham Ahmad Alizadeh1, Leila Karami2, Fahimeh Ghasemi3
1Department of Pharmacy, Eastern Mediterranean University, Famagusta, North Cyprus.
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-G1 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.
Insights
This study investigated statins as potential inhibitors of the c-Met pathway in gastric cancer. While fluvastatin and pitavastatin showed cytotoxicity, they did not significantly impact c-Met signaling.
Area of Science:
- Oncology
- Pharmacology
- Computational Biology
Background:
- The c-Met receptor tyrosine kinase is a therapeutic target in various cancers, including stomach cancer.
- Overexpression of c-Met and hepatocyte growth factor (HGF) are linked to poor prognosis.
- Statins, known for lowering LDL, possess anti-cancer properties through pleiotropic effects.
Purpose of the Study:
- To identify the most effective statin for inhibiting c-Met signaling using computational and experimental methods.
- To evaluate the anti-cancer potential of statins against gastric cancer cells.
- To explore the mechanism of statin action on the c-Met pathway.
Main Methods:
- Machine learning models were developed to predict statin activity against c-Met.
- Molecular docking and molecular dynamics simulations assessed biomolecular interactions.
- Cytotoxicity, flow cytometry, and western blot assays were performed on gastric cancer cell lines (AGS and MKN-45).
Main Results:
- Machine learning predicted high inhibitory activity (>200 nM) for proposed statin structures.
- Fluvastatin and pitavastatin demonstrated the highest inhibitory potential in silico.
- In cell-based assays, these statins induced cytotoxicity and apoptosis in gastric cancer cells, with sub-G1 DNA content accumulation.
- Western blot analysis revealed no significant reduction in c-Met phosphorylation.
Conclusions:
- Statins, including fluvastatin and pitavastatin, exhibit cytotoxic and apoptotic effects on gastric cancer cells.
- No direct inhibitory effect of these statins on the c-Met pathway phosphorylation was observed.
- The study did not establish a link between statin-induced inhibition and the c-Met pathway in cancer cells.
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