FOXC1 drives lung cancer metastasis and functions as a target for AI-enabled anti-metastatic drug discovery.

BACKGROUND: Lung cancer metastasis remains the leading cause of treatment failure and death. Forkhead box C1 (FOXC1) promotes cancer progression, but its definitive mechanism in lung cancer metastasis and druggable potential remain unclear. This study aimed to clarify the mechanism and develop a FOXC1-targeted inhibitor. METHODS: FOXC1 expression was analyzed using the HCMDB database. Metastatic function was evaluated using migration and invasion assays and experimental pulmonary metastasis models. Dual-luciferase reporter assays and ChIP-PCR were used to characterize the FOXC1/β-catenin feedback loop. Candidate FOXC1-binding compounds were generated using the MolProphet AI platform. Binding was evaluated by SPR and MST, and complex stability was assessed by molecular docking and molecular dynamics simulations. RESULTS: FOXC1 expression was elevated in metastatic lung cancer lesions. FOXC1 directly bound the β-catenin promoter, whereas β-catenin-activated TCF4 reciprocally stimulated FOXC1 transcription, forming a self-reinforcing positive-feedback loop. FOXC1-I4 bound FOXC1, reduced FOXC1-dependent reporter activity, and inhibited lung cancer cell migration, invasion, and early pulmonary colonization without significantly affecting cell viability at the tested concentrations. CONCLUSION: This study identifies a FOXC1/β-catenin positive-feedback loop that promotes lung cancer metastasis and identifies FOXC1-I4 as an AI-designed FOXC1-binding lead compound with anti-metastatic activity in the experimental models used.