Network analysis-guided drug repurposing: IGF1R as a novel melanoma target and therapeutic potential of dapagliflozin.

BACKGROUND: Skin Cutaneous Melanoma (SKCM) is an aggressive malignancy requiring novel therapeutic targets. OBJECTIVES: This study aimed to employ bioinformatics to identify key genes in melanoma pathogenesis and evaluate potential drug candidates through a repurposing strategy. METHODS: An integrated bioinformatic analysis of Gene Expression Omnibus (GEO) datasets were used to identify differentially expressed genes (DEGs). Functional enrichment and protein-protein interaction (PPI) network analyses highlighted hub genes, leading to the selection of Insulin-like Growth Factor 1 Receptor (IGF1R) as the therapeutic target. Virtual screening (DrugBank) identified Dapagliflozin and Rivaroxaban, which were further evaluated by molecular dynamics (MD) simulations, binding free energy (MM/GBSA) calculations, and in vitro cytotoxicity using the MTT assay on B16F10 melanoma cells. RESULTS: A total of 5,001 DEGs (1,030 upregulated) were identified. PPI network analysis highlighted IGF1R as a critical hub gene. MD simulations indicated Dapagliflozin formed a more stable complex with IGF1R than Rivaroxaban. MM/GBSA predicted comparable, favorable binding free energies. The MTT assay demonstrated significantly higher cytotoxic activity for Dapagliflozin (IC50 = 45 µM) against B16F10 cells compared to Rivaroxaban (IC50 > 184 µM) after 48 h. CONCLUSIONS: This study successfully utilized a bioinformatics pipeline to identify IGF1R as a key therapeutic target in melanoma. Computational analyses predicted favorable binding for Dapagliflozin to IGF1R, and subsequent in vitro experiments confirmed its moderate cytotoxic effects on melanoma cells, supporting its further investigation as a potential repurposed therapeutic agent for melanoma.