Skip to content
2000
Volume 32, Issue 9
  • ISSN: 1381-6128
  • E-ISSN: 1873-4286

Abstract

Introduction

The marine ecosystem, known for its diverse biochemistry and organisms adapted to harsh environments, contains numerous plants with promising anticancer potential. , a seagrass, contains a variety of bioactive compounds that provide various pharmacological properties. However, its potential anticancer effects against breast cancer remain largely unexplored.

Methods

HRLC-MS analysis was conducted to identify the phytochemicals in the ethanolic extract of leaves. Several publicly available databases, including SEA, STP, MALACARDS, DISGENET, and OMIM, were used to identify target genes. Protein-protein interaction (PPI) networks, gene ontology, and pathway analysis were carried out through the STRING and DAVID databases. Molecular docking was performed by Autodock Vina, while molecular dynamics (MD) simulations and MMPBSA analyses were conducted using GROMACS, demonstrating the stability of the complexes up to 200 ns.

Results

The top five therapeutically active phytochemicals were Quercetin, Arborinine, Methyl 3,4,5-trimethoxycinnamate, Citreorosein, and Scopolin. The five hub genes, AKT1, EGFR, TNF, ESR1, and GAPDH, were found by network analyses. Molecular docking and MD simulation demonstrate that Quercetin and Citreorosein are the best phytochemicals exhibiting the highest affinities to breast cancer targets AKT1, EGFR, and ESR1.

Discussion

For the first time, this study investigates the potential of citreorosein and quercetin, two phytochemicals predominantly found in leaves, to inhibit the activity of AKT1, EGFR, and ESR1. However, as these results are based on predictive computational analyses, further experimental validation is necessary to confirm their precise mechanisms of action.

Conclusion

Phytochemicals, namely Quercetin and Citreorosein, may have an impact on the progression of breast cancer by binding to the key targets AKT1, ESR1, and EGFR.

Loading

Article metrics loading...

/content/journals/cpd/10.2174/0113816128408970250717095140
2025-07-29
2026-02-22
Loading full text...

Full text loading...

References

  1. GiaquintoA.N. SungH. MillerK.D. Breast Cancer Statistics, 2022.CA Cancer J. Clin.202272652454110.3322/caac.21754 36190501
    [Google Scholar]
  2. O’SullivanC.C. LoprinziC.L. HaddadT.C. Updates in the evaluation and management of breast cancer.Mayo Clin. Proc.201893679480710.1016/j.mayocp.2018.03.025 29866283
    [Google Scholar]
  3. YiallourouA. PantavouK. MarkozannesG. Non-genetic factors and breast cancer: An umbrella review of meta-analyses.BMC Cancer202424190310.1186/s12885‑024‑12641‑8 39061008
    [Google Scholar]
  4. MartinA.M. WeberB.L. Genetic and hormonal risk factors in breast cancer.J. Natl. Cancer Inst.200092141126113510.1093/jnci/92.14.1126 10904085
    [Google Scholar]
  5. YounH.J. HanW. A review of the epidemiology of breast cancer in Asia: Focus on risk factors.Asian Pac. J. Cancer Prev.202021486788010.31557/APJCP.2020.21.4.867 32334446
    [Google Scholar]
  6. CorbenA.D. Pathology of invasive breast disease.Surg. Clin. North Am.201393236339210.1016/j.suc.2013.01.003 23464691
    [Google Scholar]
  7. AminM.B. GreeneF.L. EdgeS.B. ComptonC.C. GershenwaldJ.E. BrooklandR.K. The eighth edition AJCC cancer staging manual: Continuing to build a bridge from a population-based to a more "personalized" approach to cancer staging.In: CA Cancer J Clin.2017672939910.3322/caac.21388 28094848
    [Google Scholar]
  8. AllisonK.H. HammondM.E.H. DowsettM. Estrogen and progesterone receptor testing in breast cancer: ASCO/CAP guideline update.J. Clin. Oncol.202038121346136610.1200/JCO.19.02309 31928404
    [Google Scholar]
  9. WaksA.G. WinerE.P. Breast cancer treatment: A review.JAMA2019321328830010.1001/jama.2018.19323 30667505
    [Google Scholar]
  10. BurguinA. DiorioC. DurocherF. Breast cancer treatments: Updates and new challenges.J. Pers. Med.202111880810.3390/jpm11080808 34442452
    [Google Scholar]
  11. TrayesK.P. CokenakesS.E.H. Breast cancer treatment.Am. Fam. Physician20211042171178 34383430
    [Google Scholar]
  12. YeF. DewanjeeS. LiY. Advancements in clinical aspects of targeted therapy and immunotherapy in breast cancer.Mol. Cancer202322110510.1186/s12943‑023‑01805‑y 37415164
    [Google Scholar]
  13. SmolarzB. NowakA.Z. RomanowiczH. Breast cancer—epidemiology, classification, pathogenesis and treatment (Review of Literature).Cancers20221410256910.3390/cancers14102569 35626173
    [Google Scholar]
  14. Caswell-JinJ.L. PlevritisS.K. TianL. Change in survival in metastatic breast cancer with treatment advances: Meta-analysis and systematic review.JNCI Cancer Spectr.201824pky06210.1093/jncics/pky062 30627694
    [Google Scholar]
  15. PoorvuP.D. FrazierA.L. FeracoA.M. Cancer treatment-related infertility: A critical review of the evidence.JNCI Cancer Spectr.201931pkz00810.1093/jncics/pkz008 31360893
    [Google Scholar]
  16. HuehnchenP. van KampenA. BoehmerleW. EndresM. Cognitive impairment after cytotoxic chemotherapy.Neurooncol. Pract.202071112110.1093/nop/npz052 32257280
    [Google Scholar]
  17. ZajączkowskaR. Kocot-KępskaM. LeppertW. WrzosekA. MikaJ. WordliczekJ. Mechanisms of chemotherapy-induced peripheral neuropathy.Int. J. Mol. Sci.2019206145110.3390/ijms20061451 30909387
    [Google Scholar]
  18. SafarzadehE. Sandoghchian ShotorbaniS. BaradaranB. Herbal medicine as inducers of apoptosis in cancer treatment.Adv. Pharm. Bull.20144421427 25364657
    [Google Scholar]
  19. GreenwellM. RahmanP.K.S.M. Medicinal plants: Their use in anticancer treatment.Int. J. Pharm. Sci. Res.20156104103411210.13040/IJPSR.0975‑8232.6(10).4103‑12 26594645
    [Google Scholar]
  20. GonoC.M.P. AhmadiP. HertianiT. SeptianaE. PutraM.Y. ChianeseG. A comprehensive update on the bioactive compounds from seagrasses.Mar. Drugs202220740610.3390/md20070406 35877699
    [Google Scholar]
  21. KontizaI. VagiasC. JakupovicJ. MoreauD. RoussakisC. RoussisV. Cymodienol and cymodiene: New cytotoxic diarylheptanoids from the sea grass Cymodocea nodosa.Tetrahedron Lett.200546162845284710.1016/j.tetlet.2005.02.123
    [Google Scholar]
  22. StyshovaO.N. PopovA.M. ArtyukovA.A. KlimovichA.A. Main constituents of polyphenol complex from seagrasses of the genus Zostera, their antidiabetic properties and mechanisms of action.Exp. Ther. Med.20171351651165910.3892/etm.2017.4217 28565749
    [Google Scholar]
  23. HuaK.F. HsuH.Y. SuY.C. Study on the antiinflammatory activity of methanol extract from seagrass Zostera japonica.J. Agric. Food Chem.200654230631110.1021/jf0509658 16417284
    [Google Scholar]
  24. RossC. PuglisiM.P. PaulV.J. Antifungal defenses of seagrasses from the Indian River Lagoon, Florida.Aquat. Bot.200888213414110.1016/j.aquabot.2007.09.003
    [Google Scholar]
  25. KontizaI. StavriM. ZlohM. VagiasC. GibbonsS. RoussisV. New metabolites with antibacterial activity from the marine angiosperm Cymodocea nodosa.Tetrahedron20086481696170210.1016/j.tet.2007.12.007
    [Google Scholar]
  26. KannanR.R.R. ArumugamR. AnantharamanP. Antibacterial potential of three seagrasses against human pathogens.Asian Pac. J. Trop. Med.201031189089310.1016/S1995‑7645(10)60214‑3
    [Google Scholar]
  27. ShortF.T. ShortC.A. NovakA.B. Seagrasses. In: The Wetland Book.DordrechtSpringer2018739110.1007/978‑94‑007‑4001‑3_262
    [Google Scholar]
  28. RavillaL. Navaith AhmedS. KalaivaniP. VanithaV. A review on halodule uninervis - A potent seagrass.Int J Res Pharm Sci2020111875879
    [Google Scholar]
  29. CollierC.J. WaycottM. OspinaA.G. Responses of four Indo-West Pacific seagrass species to shading.Mar. Pollut. Bull.2012654-934235410.1016/j.marpolbul.2011.06.017 21741666
    [Google Scholar]
  30. BeheraDP NayakL Sporadic occurrence of sea grass bed, Holodule uninervis near Sidha gumpha of Chilika lagoon a good sign for biodiversity augmentation.INT J CURR SCI201517E105108
    [Google Scholar]
  31. SkeltonP.A. SouthG.R. Seagrass biodiversity of the Fiji and Samoa islands, South Pacific.N. Z. J. Mar. Freshw. Res.200640234535610.1080/00288330.2006.9517426
    [Google Scholar]
  32. WehbeN. BechelanyM. BadranA. Al-SawalmihA. MesmarJ.E. BaydounE. A Phytochemical analysis and the pharmacological implications of the seagrass Halodule uninervis: An overview.Pharmaceuticals202417899310.3390/ph17080993 39204098
    [Google Scholar]
  33. DainaA. MichielinO. ZoeteV. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules.Sci. Rep.20162017711310.1038/srep42717 28256516
    [Google Scholar]
  34. Abdullah-ZawawiM.R. GovenderN. KarimM.B. Altaf-Ul-AminM. KanayaS. Mohamed-HusseinZ.A. Chemoinformatics-driven classification of Angiosperms using sulfur-containing compounds and machine learning algorithm.Plant Methods202218111810.1186/s13007‑022‑00951‑6 36335358
    [Google Scholar]
  35. WangZ. LiangL. YinZ. LinJ. Improving chemical similarity ensemble approach in target prediction.J. Cheminform.2016812010.1186/s13321‑016‑0130‑x 27110288
    [Google Scholar]
  36. GfellerD. GrosdidierA. WirthM. DainaA. MichielinO. ZoeteV. SwissTargetPrediction: A web server for target prediction of bioactive small molecules.Nucleic Acids Res.201442W1W32-810.1093/nar/gku293 24792161
    [Google Scholar]
  37. PiñeroJ. Ramírez-AnguitaJ.M. Saüch-PitarchJ. The DisGeNET knowledge platform for disease genomics: 2019 update.Nucleic Acids Res.202048D1D845D85510.1093/nar/gkz1021 31680165
    [Google Scholar]
  38. RappaportN. TwikM. PlaschkesI. MalaCards: An amalgamated human disease compendium with diverse clinical and genetic annotation and structured search.Nucleic Acids Res.201745D1D877D88710.1093/nar/gkw1012 27899610
    [Google Scholar]
  39. AmbergerJ.S. BocchiniC.A. SchiettecatteF. ScottA.F. HamoshA. OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders.Nucleic Acids Res.201543D1D789D79810.1093/nar/gku1205 25428349
    [Google Scholar]
  40. SrinivasanM. GangurdeA. ChandaneA.Y. TagalpallewarA. PawarA. BahetiA.M. Integrating network pharmacology and in silico analysis deciphers Withaferin-A’s anti-breast cancer potential via hedgehog pathway and target network interplay.Brief. Bioinform.2024252bbae03210.1093/bib/bbae032 38446743
    [Google Scholar]
  41. SzklarczykD. KirschR. KoutrouliM. NastouK. MehryaryF. HachilifR. The STRING database in 2023: Protein-protein association networks and functional enrichment analyses for any sequenced genome of interest.Nucleic Acids Res.202351D1D638D64610.1093/nar/gkac1000 36370105
    [Google Scholar]
  42. ShannonP. MarkielA. OzierO. Cytoscape: A software environment for integrated models of biomolecular interaction networks.Genome Res.200313112498250410.1101/gr.1239303 14597658
    [Google Scholar]
  43. ShermanB.T. HaoM. QiuJ. DAVID: A web server for functional enrichment analysis and functional annotation of gene lists (2021 update).Nucleic Acids Res.202250W1W216-2110.1093/nar/gkac194 35325185
    [Google Scholar]
  44. GuoX. SuL. ShiM. Network pharmacology and transcriptomics to explore the pharmacological mechanisms of 20(S)-Protopanaxatriol in the treatment of depression.Int. J. Mol. Sci.20242514757410.3390/ijms25147574 39062817
    [Google Scholar]
  45. JiaoY. ShiC. SunY. Unraveling the role of Scutellaria baicalensis for the treatment of breast cancer using network pharmacology, molecular docking, and molecular dynamics simulation.Int. J. Mol. Sci.2023244359410.3390/ijms24043594 36835006
    [Google Scholar]
  46. WuW.I. VoegtliW.C. SturgisH.L. DizonF.P. VigersG.P.A. BrandhuberB.J. Crystal structure of human AKT1 with an allosteric inhibitor reveals a new mode of kinase inhibition.PLoS One2010591291310.1371/journal.pone.0012913 20886116
    [Google Scholar]
  47. StamosJ. SliwkowskiM.X. EigenbrotC. Structure of the epidermal growth factor receptor kinase domain alone and in complex with a 4-anilinoquinazoline inhibitor.J. Biol. Chem.200227748462654627210.1074/jbc.M207135200 12196540
    [Google Scholar]
  48. ShiauA.K. BarstadD. LoriaP.M. The structural basis of estrogen receptor/coactivator recognition and the antagonism of this interaction by tamoxifen.Cell199895792793710.1016/S0092‑8674(00)81717‑1 9875847
    [Google Scholar]
  49. JenkinsJ.L. TannerJ.J. High-resolution structure of human D -glyceraldehyde-3-phosphate dehydrogenase.Acta Crystallogr. D Biol. Crystallogr.200662329030110.1107/S0907444905042289 16510976
    [Google Scholar]
  50. HeM.M. SmithA.S. OslobJ.D. FlanaganW.M. BraistedA.C. WhittyA. Small-molecule inhibition of TNF-α.Science20053101022102510.1126/science.1116304
    [Google Scholar]
  51. AbrahamM.J. MurtolaT. SchulzR. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers.SoftwareX20151-2192510.1016/j.softx.2015.06.001
    [Google Scholar]
  52. HuangJ. MacKerellA.D. CHARMM36 all-atom additive protein force field: Validation based on comparison to NMR data.J. Comput. Chem.201334252135214510.1002/jcc.23354 23832629
    [Google Scholar]
  53. MaruyamaY. IgarashiR. UshikuY. MitsutakeA. Analysis of protein folding simulation with moving root mean square deviation.J. Chem. Inf. Model.20236351529154110.1021/acs.jcim.2c01444 36821519
    [Google Scholar]
  54. TiburuE.K. IssahI. DarkoM. Investigating the conformation of S100β protein under physiological parameters using computational modeling: A clue for rational drug design.Open Biomed. Eng. J.2018121365010.2174/1874120701812010036 30069254
    [Google Scholar]
  55. AhmedM.C. CrehuetR. Lindorff-LarsenK. Computing, analyzing, and comparing the radius of gyration and hydrodynamic radius in conformational ensembles of intrinsically disordered proteins.Methods Mol. Biol.2020214142944510.1007/978‑1‑0716‑0524‑0_21 32696370
    [Google Scholar]
  56. EisenhaberF. LijnzaadP. ArgosP. SanderC. ScharfM. The double cubic lattice method: Efficient approaches to numerical integration of surface area and volume and to dot surface contouring of molecular assemblies.J. Comput. Chem.199516327328410.1002/jcc.540160303
    [Google Scholar]
  57. Valdés-TresancoM.S. Valdés-TresancoM.E. ValienteP.A. MorenoE. gmx_MMPBSA: A new tool to perform end-state free energy calculations with GROMACS.J. Chem. Theory Comput.202117106281629110.1021/acs.jctc.1c00645 34586825
    [Google Scholar]
  58. SinghV. RamM. KumarR. PrasadR. RoyB.K. SinghK.K. Phosphorylation: Implications in cancer.Protein J.20173611610.1007/s10930‑017‑9696‑z 28108801
    [Google Scholar]
  59. RajagopalT. SeshachalamA. RathnamK.K. Impact of xenobiotic-metabolizing gene polymorphisms on breast cancer risk in South Indian women.Breast Cancer Res. Treat.2021186382383710.1007/s10549‑020‑06028‑z 33392841
    [Google Scholar]
  60. YangY. KarakhanovaS. WernerJ. BazhinA. Reactive oxygen species in cancer biology and anticancer therapy.Curr. Med. Chem.201320303677369210.2174/0929867311320999165 23862622
    [Google Scholar]
  61. ScaliaA. DoumaniL. KindtN. JournéF. TrelcatA. CarlierS. The interplay between atherosclerosis and cancer: Breast cancer cells increase the expression of endothelial cell adhesion markers.Biology202312789610.3390/biology12070896 37508329
    [Google Scholar]
  62. GuoY.J. PanW.W. LiuS.B. ShenZ.F. XuY. HuL.L. ERK/MAPK signalling pathway and tumorigenesis.Exp. Ther. Med.20201931997200710.3892/etm.2020.8454 32104259
    [Google Scholar]
  63. MiricescuD. TotanA. Stanescu-SpinuI-I. Constantin BadoiuS. StefaniC. GreabuM. PI3K/AKT/mTOR signaling pathway in breast cancer: From molecular landscape to clinical aspects.In: Int J Mol Sci.202022117310.3390/ijms22010173 33375317
    [Google Scholar]
  64. ChengH. ShcherbaM. PendurtiG. LiangY. PiperdiB. Perez-SolerR. Targeting the PI3K/AKT/mTOR pathway: Potential for lung cancer treatment.Lung Cancer Manag.201431677510.2217/lmt.13.72 25342981
    [Google Scholar]
  65. GeorgeB. GuiB. RaguramanR. AKT1 transcriptomic landscape in breast cancer cells.Cells20221115229010.3390/cells11152290 35892586
    [Google Scholar]
  66. LiuX. Adorno-CruzV. ChangY.F. EGFR inhibition blocks cancer stem cell clustering and lung metastasis of triple negative breast cancer.Theranostics202111136632664310.7150/thno.57706 33995681
    [Google Scholar]
  67. SongX. LiuZ. YuZ. EGFR promotes the development of triple negative breast cancer through JAK/STAT3 signaling.Cancer Manag. Res.20201270371710.2147/CMAR.S225376 32099467
    [Google Scholar]
  68. LiuW. LuX. ShiP. TNF-α increases breast cancer stem-like cells through up-regulating TAZ expression via the non-canonical NF-κB pathway.Sci. Rep.2020101180410.1038/s41598‑020‑58642‑y 31913322
    [Google Scholar]
  69. GeertsD. CusickJ.K. ConnellyL. Editorial: The tumor necrosis factor superfamily: An increasing role in breast cancer.Front. Oncol.20201062258810.3389/fonc.2020.622588 33415081
    [Google Scholar]
  70. NagyZ. JeselsohnR. ESR1 fusions and therapeutic resistance in metastatic breast cancer.Front. Oncol.202312January103753110.3389/fonc.2022.1037531 36686845
    [Google Scholar]
  71. ZhangJ.Y. ZhangF. HongC.Q. Critical protein GAPDH and its regulatory mechanisms in cancer cells.Cancer Biol. Med.2015121102210.7497/j.issn.2095‑3941.2014.0019 25859407
    [Google Scholar]
  72. HigashimuraY. NakajimaY. YamajiR. Up-regulation of glyceraldehyde-3-phosphate dehydrogenase gene expression by HIF-1 activity depending on Sp1 in hypoxic breast cancer cells.Arch. Biochem. Biophys.201150911810.1016/j.abb.2011.02.011 21338575
    [Google Scholar]
  73. LuY. SuhS.J. LiX. Citreorosein inhibits production of proinflammatory cytokines by blocking mitogen activated protein kinases, nuclear factor-κB and activator protein-1 activation in mouse bone marrow-derived mast cells.Biol. Pharm. Bull.201235693894510.1248/bpb.35.938 22687535
    [Google Scholar]
  74. SuL. XuC. HuangH. Effects of tumor necrosis factor-alpha inhibitors on lipid profiles in patients with psoriasis: A systematic review and meta-analysis.Front. Immunol.202415135459310.3389/fimmu.2024.1354593 38500874
    [Google Scholar]
  75. JiangJ. YangY. WangF. MaoW. WangZ. LiuZ. Quercetin inhibits breast cancer cell proliferation and survival by targeting Akt/mTOR/PTEN signaling pathway.Chem. Biol. Drug Des.202410361455710.1111/cbdd.14557 38825578
    [Google Scholar]
  76. SenthilkumarK. ArunkumarR. ElumalaiP. Quercetin inhibits invasion, migration and signalling molecules involved in cell survival and proliferation of prostate cancer cell line (PC‐3).Cell Biochem. Funct.2011292879510.1002/cbf.1725 21308698
    [Google Scholar]
  77. SelvakumarP. BadgeleyA. MurphyP. Flavonoids and other polyphenols act as epigenetic modifiers in breast cancer.Nutrients202012376110.3390/nu12030761 32183060
    [Google Scholar]
  78. GangulyS. AroraI. TollefsbolT.O. Impact of stilbenes as epigenetic modulators of breast cancer risk and associated biomarkers.Int. J. Mol. Sci.202122181003310.3390/ijms221810033 34576196
    [Google Scholar]
  79. HumphriesB. WangZ. YangC. MicroRNA regulation of epigenetic modifiers in breast cancer.Cancers201911789710.3390/cancers11070897 31252590
    [Google Scholar]
  80. BuschC. BurkardM. LeischnerC. LauerU.M. FrankJ. VenturelliS. Epigenetic activities of flavonoids in the prevention and treatment of cancer.Clin. Epigenetics2015716410.1186/s13148‑015‑0095‑z 26161152
    [Google Scholar]
  81. NimalS. KumbharN. PoteM.S. BankarR. ShaikhM. GaccheR. Reversal of epithelial to mesenchymal transition in triple negative breast cancer through epigenetic modulations by dietary flavonoid Galangin and its combination with SAHA.Cell Commun. Signal.202523116310.1186/s12964‑025‑02174‑3 40176095
    [Google Scholar]
/content/journals/cpd/10.2174/0113816128408970250717095140
Loading
/content/journals/cpd/10.2174/0113816128408970250717095140
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher’s website along with the published article.

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test