Deciphering the multi-target anticancer potential of β-sitosterol in breast cancer through integrated computational approaches

Abstract

Breast cancer is a complex disease comprising multiple deregulated signaling pathways, oxidative stress, metabolic rewiring, and resistance to therapy. The multi-target therapeutic efficacy of β-sitosterol against breast cancer was studied using an integrated approach that combined network pharmacology, molecular docking, molecular dynamics simulations, and ADMET. Out of which 98 common targets were identified between β-sitosterol and breast cancer, wherein PPARG, TNF, ABL kinase, HIF1A, ESR1, PGR, PPARA, MAPK8, AR, and ESR2 are the key hub genes. The enrichment analysis showed that β-Sitosterol had strong binding affinities towards ABL kinase (-9.7 kcal/mol), PPARA (-9.5 kcal/mol), MAPK8 (-8.7 kcal/mol), and PPARG (-8.6 kcal/mol). Indeed, molecular dynamics simulations were performed for 1000 ns at the molecular level, and the progesterone receptor (PGR) proved to be the most dynamically stable target, as the β-sitosterol-PGR complex remained stable throughout the simulation. The predicted ADMET profile was good. The results suggest that β-sitosterol acts on multiple targets in breast cancer and identify PGR, a target not strongly favored by docking alone, as an important therapeutic target revealed through extended molecular dynamics simulation.