In silico evaluation of phytocompounds targeting drug resistance mechanisms in triple-negative breast cancer: Insights from GEO dataset analysis

Triple-Negative Breast Cancer (TNBC) is one of the most aggressive and heterogeneous subtypes of breast cancer, characterized by poor prognosis and often develops resistance to conventional therapy. The current study employs an integrative in silico approach to identify and evaluate the potential of phytocompounds to target genes or proteins associated with drug resistance in TNBC. This work uses high-throughput sequencing expression profiling datasets from the NCBI Gene Expression Omnibus (GEO) database to identify differentially expressed genes. Protein-protein interaction network analyses were employed further to understand the interconnectivity of these genes, ultimately narrowing down potential druggable targets (CDK1, MAOA, DHFR, TYMS). Selected phytochemicals were screened against identified target proteins using pharmacokinetic profiling (ADMET) and molecular docking. Our findings suggest that kirenol, artesunate, apigenin, daidzein, and sclareol have the potential to target specific genes involved in chemoresistance in TNBC and can serve as therapeutic options against drug-resistant TNBC.