Network analysis and drug repurposing reveal targets for combination therapy in drug resistant breast cancer
Multidrug resistance remains a major obstacle in cancer therapy and is driven by hyperactive efflux transporters and dysregulated signaling pathways. In this study, text mining and network analyses were performed to identify key genes associated with breast cancer drug resistance. Critical regulatory nodes were identified using the Boykov-Kolmogorov algorithm applied to a directed protein–protein interaction network. Molecular docking and molecular dynamics simulations were subsequently conducted to screen FDA-approved drugs for potential interactions with these targets. Cytotoxicity, migration, apoptosis, efflux activity, and relative gene expression assays were performed in drug-resistant and parental breast and gastric cancer cell lines to experimentally evaluate drug effects. ESR1, PPARD, and NFKB1 were identified as essential cut nodes sustaining MDR network connectivity. Drug repurposing analyses predicted celecoxib, desloratadine, and dutasteride as ligands targeting these proteins. Experimental validation demonstrated that the triple-drug combination significantly increased mitoxantrone sensitivity ( P < 0.001) in multidrug-resistant breast cancer cells. This effect was accompanied by marked inhibition of drug efflux, including significant suppression of BCRP activity ( P < 0.05) and up to a 20-fold reduction in BCRP gene expression ( P < 0.001), while more limited effects were observed on MDR1 expression in gastric cells. Collectively, the combination treatment restored chemotherapy responsiveness, reduced cell migration, and promoted apoptosis. Overall, this study suggests that integrating network-based analysis with drug repurposing may provide a useful framework for identifying potential multidrug strategies against drug resistance. These findings offer a computational basis for further experimental validation and potential development of anti-resistance therapeutic approaches.