Anticancer activity of lichen-derived compounds against triple-negative breast cancer: integrated in vitro evaluation, network pharmacology, molecular docking, and KEGG pathway analysis.

Integrating in silico modeling and experimental validation to determine the anti-breast cancer mechanisms of mulberry leaf bioactive compounds.

Breast cancer (BRCA) is the most prevalent malignancy and leading cause of cancer-related death in women. To elucidate the anti-BRCA mechanisms of mulberry leaves (ML), the present study used an integrated strategy combining network pharmacology, molecular docking and in vitro validation. The active components of ML, their putative targets and BRCA-related proteins were screened from public databases (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, and Encyclopedia of Traditional Chinese Medicine). A protein-protein interaction network was constructed using Cytoscape, followed by Gene Ontology (GO) biological function and Kyoto Encyclopedia of Genes and Genes (KEGG) pathway analyses through the Database for Annotation, Visualization and Integrated Discovery. Molecular docking was then conducted to assess the binding affinities of key compounds to prioritized targets. In vitro assays (MTT, wound healing and flow cytometry) were performed to evaluate the effects of ML on MDA-MB-231 cell proliferation, migration, apoptosis and cell cycle progression. Western blotting was conducted to validate core target proteins. In total, nine bioactive ML compounds (such as quercetin and stigmasterol) were identified, targeting six core proteins [tumor protein p53, estrogen receptor 1 (ESR1), catenin β1, AKT serine/threonine kinase 1, MYC proto-oncogene/BHLH transcription factor and telomerase reverse transcriptase]. GO analysis revealed enrichment in 'nucleoplasm', 'enzyme binding' and 'cellular response to hypoxia' terms, whilst KEGG pathway analysis results highlighted 'cancer-related pathways' (such as PI3K/AKT). Stigmasterol exhibited the strongest binding affinity to ESR1 (in silico). In vitro, ML extract significantly suppressed MDA-MB-231 proliferation and migration, induced apoptosis and triggered G2/M arrest. Western blotting confirmed the downregulation of oncoproteins (TP53, AKT1, ESR1 and JUN). The results of the present study suggest that ML can inhibit the proliferation and migration of BRCA cells whilst inducing their apoptosis by regulating the expression of key proteins. This provides a scientific basis for the in-depth exploration of the anti-BRCA mechanism of ML.

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.

Active ingredients and molecular targets of Tripterygium wilfordii against colorectal cancer: network pharmacology, molecular docking, molecular dynamics simulations and in vitro evaluation

Tryptophenolide (Lei4) showed moderate but reproducible anti-CRC activity and directly inhibited EGFR kinase activity in vitro, supporting EGFR pathway inhibition as one mechanism contributing to its cellular effects.

RRx-001 Accelerates Low Dose Oxaliplatin-Induced Cytotoxicity in Triple-Negative Breast Cancer Cells via Synergistic Antitumor Mechanisms

Background: Triple-negative breast cancer (TNBC) represents an aggressive subtype that has few therapeutic options for targeting and is highly resistant to chemotherapy. The compound RRx-001 is a dinitroazetidine compound with both immune-modulating and epigenetic properties, whereas oxaliplatin elicits immunogenic cell death. Objective: To evaluate the synergistic cytotoxic effects of RRx-001 combined with oxaliplatin in MDA-MB-231 TNBC cells. Methods: Viability assays using the MTT assay were performed. The IC50 concentrations were derived from dose response curves. The drug interaction analysis was completed utilizing the Chou-Talalay method, along with CompuSyn software to calculate the combination index (CI) and dose reduction index (DRI). The intracellular reactive oxygen species (ROS) levels were assayed by performing a fluorometric assay with DCFH-DA. Results: RRx-001 exhibited greater cytotoxicity (IC50=9.878 µM) than did oxaliplatin (IC50=202.2 µM). When the two drugs were used, the combination resulted in a significant decrease in the IC50 value for oxaliplatin (to 9.705 µM, ≈20.9-fold reduction). A strong synergy was seen (CI=0.0227 at Fa=0.05) with significant reductions in the doses required for each drug. Additionally, a combination of these two drugs significantly increases amounts of ROS compared to either drug alone. Conclusions: RRx-001 has been shown to significantly enhance the cytotoxicity of oxaliplatin through synergistic and ROS-mediated pathways, suggesting that it may represent a viable option for a dose-sparing combinatorial therapeutic approach to treat TNBC.

In silico and in vitro evaluation of synergistic anticancer effects of erucin and kaempferol from Eruca sativa against human A549 non-small cell lung cancer cells (NSCLC)

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

The combined computational and experimental approach enabled the identification of peptides with selective cytotoxic effects and favorable predicted immunogenic profiles, demonstrating that integrating computational screening with experimental validation is an effective strategy for accelerating the discovery of selective anticancer peptides.