Deciphering the Multi-Target Anti-Breast Cancer Mechanisms of Vernonia amygdalina (Bitter Leaf) through Network Pharmacology and Molecular Docking
Barre latérale de l'article
Contenu principal de l'article
Résumé
Background: Breast cancer remains the leading cause of cancer-related mortality among women globally, necessitating novel multi-target therapeutic strategies. This study employed an integrated network pharmacology and molecular docking approach to elucidate the anti-breast cancer mechanisms of Vernonia amygdalina (bitter leaf).
Methods: Five bioactive phytochemicals, 11,13-dihydrovernodalin, hydroxyvernolide, vernodalin, vernolide, and vernomygdin were identified following ADMET and drug-likeness screening.
Results: Venn diagram analysis revealed 21 overlapping targets between V. amygdalina phytochemicals and breast cancer-associated genes. Protein–protein interaction network analysis identified Aurora Kinase A (AURKA) and Thymidylate Synthase (TYMS) as principal hub proteins (PPI enrichment p = 9.18 × 10??). Gene Ontology and KEGG pathway enrichment analyses implicated kinase activity, mitotic spindle regulation, and the PI3K-Akt, MAPK, and Ras signaling cascades as key modulated functions and pathways. Molecular docking confirmed strong binding affinities, with hydroxyvernolide and vernomygdin exhibiting the highest affinity toward TYMS (?8.6 kcal/mol) and 11,13-dihydrovernodalin toward AURKA (?8.0 kcal/mol).
Conclusion: These findings demonstrate that V. amygdalina exerts anti-breast cancer effects through a multi-component, multi-target mechanism, providing a strong computational rationale for further experimental validation.
Téléchargements
Renseignements sur l'article
Cette œuvre est sous licence Creative Commons Attribution - Pas d'Utilisation Commerciale - Pas de Modification 4.0 International.
Références
World Health Orgnization . (2024). Breast cancer. https://www.who.int/news-room/fact-sheets/detail/breast-cancer
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. (2021) 71:209–49. doi: 10.3322/caac.21660, PMID: [DOI] [PubMed] [Google Scholar]
Wilkinson L, Gathani T. Understanding breast cancer as a global health concern. Br J Radiol. (2022) 95:20211033. doi: 10.1259/bjr.20211033, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
Gersten O, Wilmoth JR. The cancer transition in Japan since 1951. Demogr Res. (2002) 7:271–306. doi: 10.4054/DemRes.2002.7.5 [DOI] [Google Scholar]
Abdel RO. The epidemiologic transition: a theory of the epidemiology of population change. Bulletin of the World Health Organization 79.2 (2021): 161–170. [PMC free article] [PubMed]
Zhao J, Xu L, Sun J, Song M, Wang L, Yuan S, et al. Global trends in incidence, death, burden and risk factors of early-onset cancer from 1990 to 2019. BMJ Oncol. (2023) 2:e000049. doi: 10.1136/bmjonc-2023-000049, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
Kaur C, Kumar S, Singh S, et al. Cancer: a black spot to human race! Res J Sci Technol. 2020;12(1):1–12. [Google Scholar]
Usunobun, U., & Ngozi, O. (2016). Phytochemical analysis and proximate composition of Vernonia amygdalina. International Journal of Scientific World, 4(1), 11-14. https://doi.org/10.14419/ijsw.v4i1.5845
Chandran U, Mehendale N, Patil S, et al. Network pharmacology. Innov Approaches Drug Discov 2017:127-164.
Noor F, ul Qamar M, Ashfaq UA, et al. Network pharmacology approach for medicinal plants: Review and assessment. Pharmaceuticals 2022;15(5):572.
Yang M, Chen JL, Xu LW, et al. Navigating traditional Chinese medicine network pharmacology and computational tools. Evid Based Complement Alternat Med 2013;2013:731969.
Li L, Yang L, Yang L, et al. Network pharmacology: A bright guiding light on the way to explore the personalized precise medication of traditional Chinese medicine. Chin Med 2023;18(1):146.
Tallei TE, Fatimawali, Adam AA, et al. Molecular insights into the anti-inflammatory activity of fermented pineapple juice using multimodal computational studies. Arch Pharm (Weinheim) 2024;357(1):e2300422.
Pendong CHA, Suoth EJ, Fatimawali F, et al. Network pharmacology approach to understanding the antidiabetic effects of pineapple peel hexane extract. Malacca Pharm 2024;2(1):24-32.
Xiong, Z., Lo, H.P., McMahon, K.A., Martel, N., Jones, A., Hill, M.M., Parton, R.G., Hall, T.E. (2021) In vivo proteomic mapping through GFP-directed proximity-dependent biotin labelling in zebrafish. eLIFE.
Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., Zaslavsky, L., Zhang, J., & Bolton, E. E. (2023). PubChem 2023 update: improved data content and web interfaces. Nucleic Acids Research, 51(D1), D1373–D1380. https://doi.org/10.1093/nar/gkac956
Daina, A., Michielin, O., & Zoete, V. (2019). SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Research, 47(W1), W357–W364. https://doi.org/10.1093/nar/gkz382
Safran, M., Rosen, N., Twik, M., et al. (2021). The GeneCards Suite. In Practical Guide to Life Science Databases. Springer. https://doi.org/10.1007/978-981-16-5812-9_21
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., ... & Ideker, T. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research, 13(11), 2498-2504.https://doi.org/10.1101/gr.1239303 PMID: 14597658
Oliveros, J. C. (2015). Venny 2.1: An interactive tool for comparing lists with Venn diagrams. Retrieved from https://bioinfogp.cnb.csic.es/tools/venny/
Szklarczyk, D., Kirsch, R., Koutrouli, M., Nastou, K., Mehryary, F., Hachilif, R., ... & Von Mering, C. (2023). The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic acids research, 51(D1), D638-D646. https://doi.org/10.1093/nar/gkac1000
Ge, S. X., Jung, D., & Yao, R. (2020). ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics, 36(8), 2628–2629. https://doi.org/10.1093/bioinformatics/btz931
Jane?ek, M., Rossmann, M., Sharma, P. et al. Allosteric modulation of AURKA kinase activity by a small-molecule inhibitor of its protein-protein interaction with TPX2. Sci Rep 6, 28528 (2016). https://doi.org/10.1038/srep28528
Czyzyk DJ, Valhondo M, Jorgensen WL, Anderson KS. Understanding the structural basis of species selective, stereospecific inhibition for Cryptosporidium and human thymidylate synthase. FEBS Lett. 2019 Aug;593(15):2069-2078. doi: 10.1002/1873-3468.13474. Epub 2019 Jun 18. PMID: 31172516; PMCID: PMC6690774.
Burley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chen, L., Crichlow, G. V.,. & Zhuravleva, M. (2021). RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic acids research, 49(D1), D437-D451. https://doi.org/10.1093/nar/gkaa1038
Fu, L., Shi, S., Wu, C., Zeng, X., Cao, D., & Hou, T. (2024). ADMETlab 3.0: An updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support. Nucleic Acids Research, 52(W1), W422–W431. https://doi.org/10.1093/nar/gkae236
Li Y, Lu D, Xu A. Abstract TP276: Identification of Potential Key Genes and Pathways in Ischemic Stroke by Analysis of Human High Throughput Sequencing Data. Stroke 2020;51(1). ATP276-ATP276.
Wang H, Li Y, Liu Y. ADMET and drug-likeness prediction of natural compounds with anti-inflammatory potential: An in silico study. J Biomolecul Struct Dyn 2021; 39(18):6827–39. https://doi.org/10.1080/07391102.2020.1806898.
Oncology Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–674.
Molecular Biology Fu L, Kettner NM. The circadian clock in cancer development and therapy. Progress in Molecular Biology and Translational Science. 2013;119:221–282.
Cancer Research Barker N, Clevers H. Tracking down the stem cells of the intestine: strategies to identify adult stem cells. Gastroenterology. 2007;133(6):1755–1760.
Molecular Biology Manning G, Whyte DB, Martinez R, Hunter T, Sudarsanam S. The protein kinase complement of the human genome. Science. 2002;298(5600):1912–1934.
Molecular Oncology Hanker AB, Sudhan DR, Arteaga CL. Overcoming endocrine resistance in breast cancer. Cancer Cell. 2020;37(4):496–513.
Saddala MS, Huang H. Identification of novel inhibitors of tumor necrosis factor alpha using combined ligand-based and structure-based virtual screening approaches. J Molecul Graph Modell 2019;92:238–49. https://doi.org/10.1016/j. Jmgm.2019.07.008.
Kim JY, Park SJ, Yun KJ. Alpha- and beta-amyrin inhibit inflammatory responses mediated by TLR4/NF-?B signaling pathway in macrophages. Planta Med 2016;82 (7):603–9. https://doi.org/10.1055/s-0042-101925
Daina A, Michielin O, Zoete V. SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Research. 2019;47(W1):W357–W364.
Xiong G, Wu Z, Yi J, et al. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Research. 2021;49(W1):W5–W14.
Nguyen NH, Nguyen MT, Little PJ, et al. Vernolide-A and Vernodaline: sesquiterpene lactones with cytotoxicity against cancer. Journal of Environmental Pathology, Toxicology and Oncology. 2020;39(4):299–308.
Luo Y, Zhang D, Hou L, Lin N. Vernodalin suppresses tumor proliferation and increases apoptosis of gastric cancer cells through attenuation of FAK/PI3K/AKT/mTOR and MAPKs signaling pathways. Current Pharmaceutical Biotechnology. 2023;24(5):708–717.
Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nature Chemical Biology. 2008;4(11):682–690.
Li S, Zhang B. Traditional Chinese medicine network pharmacology: theory, methodology and application. Chinese Journal of Natural Medicines. 2013;11(2):110–120.
Nikonova AS, Astsaturov I, Serebriiskii IG, Dunbrack RL Jr, Golemis EA. Aurora A kinase (AURKA) in normal and pathological cell growth. Cellular and Molecular Life Sciences. 2013;70:661–687.
Du R, Huang C, Liu K, Li X, Dong Z. Targeting AURKA in cancer: molecular mechanisms and opportunities for cancer therapy. Molecular Cancer. 2021;20:15.
Longley DB, Harkin DP, Johnston PG. 5-Fluorouracil: mechanisms of action and clinical strategies. Nature Reviews Cancer. 2003;3(5):330–338.
Fu L, Kettner NM. The circadian clock in cancer development and therapy. Progress in Molecular Biology and Translational Science. 2013;119:221–282.
Compton DA. Mechanisms of aneuploidy. Current Opinion in Cell Biology. 2011;23(1):109–113.
Manning G, Whyte DB, Martinez R, Hunter T, Sudarsanam S. The protein kinase complement of the human genome. Science. 2002;298(5600):1912–1934.
Hanker AB, Sudhan DR, Arteaga CL. Overcoming endocrine resistance in breast cancer. Cancer Cell. 2020;37(4):496–513.
, , , ,