Therapeutic Potential of Icariin in Breast Cancer: An Integrated Network Pharmacology and Molecular Simulations Approach

Genistein as a Potential Breast Cancer Therapeutic: Computational Investigations Targeting EGFR and Oncogenic Pathways

Breast cancer (BC), a malignant disease responsible for high fatality worldwide, is characterized by EGFR overexpression or mutation, which contributes to tumor cell survival and progression. The present study explored the efficacy of Genistein in modulating EGFR using an integrated computational approach. Common targets of Genistein (SwissTargetPrediction) and BC (GeneCards) were identified, followed by PPI, GO, and KEGG enrichment analyses using STRING. The Genistein–targets–pathways network was constructed using Cytoscape 3.10.0. Docking, MM/GBSA binding free‐energy calculations, molecular dynamics (MD) simulations, and post‐MD analyses (PCA and FEL) were performed for Genistein and the reference inhibitor Afatinib against EGFR. Docking scores of Genistein (−8.96 kcal/mol) and Afatinib (−10.22 kcal/mol) demonstrated comparable binding within the EGFR ATP‐binding pocket, with Genistein retaining interactions with key catalytic residues. The MM/GBSA binding free‐energy of Genistein (−134.96 kcal/mol) and Afatinib (−137.92 kcal/mol) differed only marginally, indicating comparable binding stability within the EGFR domain. MD simulations, together with RMSD, RMSF, radius of gyration, SASA, PCA, and FEL analyses, confirmed the structural stability of the Genistein‐EGFR complex throughout the simulation. Collectively, these findings suggest that Genistein is a promising multitarget EGFR‐modulating compound that warrants further experimental validation through in vitro and in vivo studies.

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

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.

Computational identification of natural phytochemicals as potential inhibitors of UBE2J1 causing ovarian cancer

Findings highlight Withaferin A as a promising natural inhibitor of UBE2J1 and provide a foundation for future experimental validation aimed at developing targeted therapies against ovarian cancer.

Integrated in silico pharmacology of Pleurotus ostreatus-derived bioactive compounds targeting EGFR using network pharmacology and molecular

Integrated Computational and Experimental Discovery of Antitumoral Peptides Targeting Luminal A and Triple-Negative Breast Cancer

Breast cancer remains one of the leading causes of cancer-related mortality worldwide, highlighting the need for more selective and effective therapies. This study aimed to identify and validate novel anticancer peptides targeting Luminal A (MCF-7) and triple-negative (MDA-MB-231) breast cancer subtypes using an integrated in silico and in vitro approach. Overexpressed proteins in each cell line were identified through literature mining, followed by the construction of protein–protein interaction (PPI) networks using STRING. Conserved motifs within PPI components were identified via multiple sequence alignment using the MEME Suite. Immunogenicity and anticancer potential of the selected motifs were predicted using VaxiJen and AntiCP, respectively. Peptide–target interactions were assessed through molecular docking (PatchDock/FireDock) and refined using molecular dynamics simulations in GROMACS. Based on these analyses, two lead peptides per subtype were selected, synthesized, and experimentally evaluated for cytotoxicity and subcellular localization. Two lead peptides were identified for each breast cancer subtype. Notably, the peptides RVCGDRGFFF and WYLKMMWQW exhibited strong membrane-associated interactions and significantly reduced the viability of MDA-MB-231 cells by approximately 75% and 90%, respectively. The combined computational and experimental approach enabled the identification of peptides with selective cytotoxic effects and favorable predicted immunogenic profiles. These findings demonstrate that integrating computational screening with experimental validation is an effective strategy for accelerating the discovery of selective anticancer peptides. The identified candidates represent promising leads for the development of peptide-based therapeutics targeting specific breast cancer subtypes, with potential applications in oncology.

Mechanism of isorhamnetin in treating triple-negative breast cancer based on network pharmacology, molecular docking, and in vitro verification

Introduction: Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer with poor prognosis and limited targeted therapies. The development of novel effective drugs is of great significance for TNBC treatment.Objective: This study aims to clarify the molecular mechanisms through which isorhamnetin(ISO) restrains the malignant progression of TNBC, usingcomputational pharmacology prediction and empirical verification.Methods: We systematically screened public online databases and identified 198 putative targets of isorhamnetin and 1,490 genes functionally associated with TNBC. Overlapping targets were obtained through Venn diagram analysis. By integrating protein–protein interaction network profiling, functional enrichment analysis, and molecular docking experiments, we predicted the key binding targets of isorhamnetin. We then validated the effects of isorhamnetin on cell proliferation, migration, and invasion in TNBC cell lines. Western blot and cellular thermal shift assays were employed to examine target protein expression and alterations in downstream signaling pathways.Results: The combination of protein–protein interaction network profiling, functional enrichment analysis, and molecular docking experiments indicated that ISO binds epidermal growth factor receptor (EGFR) with high affinity. Our findings revealed that ISO strongly reduced the growth, motility, and infiltrative capacities of TNBC cells. Western blot and cellular thermal shift assays confirmed that ISO downregulates EGFR protein expression and modulates downstream mammalian target of rapamycin (mTOR) and MAPK/ERK kinase (MEK)/extracellular signalregulated kinase (ERK) signaling.Conclusion: ISO suppresses TNBC progression by downregulating EGFR signaling, providing a theoretical basis for its clinical application.