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A Network Pharmacology-Based Analysis of Multi-Target, Multi-Pathway, Multi-Compound Treatment for Ovarian Serous Cystadenocarcinoma

  • Dan-dan Xiong
  • Yue Qin
  • Wen-qing Xu
  • Rong-quan He
  • Hua-yu Wu
  • Dan-min Wei
  • Jing-jing Zeng
  • Yi-wu Dang
  • Gang Chen
Original Research Article
  • 10 Downloads

Abstract

Background and Objectives

Pharmacological control against ovarian serous cystadenocarcinoma has received increasing attention. The purpose of this study was to investigate multi-drug treatments as synergetic therapy for ovarian serous cystadenocarcinoma and to explore their mechanisms of action by the network pharmacology method.

Methods

Genes acting on ovarian serous cystadenocarcinoma were first collected from GEPIA and DisGeNET. Gene Ontology annotation, Kyoto Encyclopedia of Genes and Genomes pathway, Reactome pathway, and Disease Ontology analyses were then conducted. A connectivity map analysis was employed to identify compounds as treatment options for ovarian serous cystadenocarcinoma. Targets of these compounds were obtained from the Search Tool for Interacting Chemicals (STITCH). The intersections between the ovarian serous cystadenocarcinoma-related genes and the compound targets were identified. Finally, the Kyoto Encyclopedia of Genes and Genomes and Reactome pathways in which the overlapped genes participated were selected, and a correspondence compound-target pathway network was constructed.

Results

A total of 541 ovarian serous cystadenocarcinoma-related genes were identified. The functional enrichment and pathway analyses indicated that these genes were associated with critical tumor-related pathways. Based on the connectivity map analysis, five compounds (resveratrol, MG-132, puromycin, 15-delta prostaglandin J2, and valproic acid) were determined as treatment agents for ovarian serous cystadenocarcinoma. Next, 48 targets of the five compounds were collected. Following mapping of the 48 targets to the 541 ovarian serous cystadenocarcinoma-related genes, we identified six targets (PTGS1, FOS, HMOX1, CASP9, PPARG, and ABCB1) as therapeutic targets for ovarian serous cystadenocarcinoma by the five compounds. By analysis of the compound-target pathway network, we found the synergistic anti-ovarian serous cystadenocarcinoma potential and the underlying mechanisms of action of the five compounds.

Conclusion

In summary, latent drugs against ovarian serous cystadenocarcinoma were acquired and their target actions and pathways were determined by the network pharmacology strategy, which provides a new prospect for medicamentous therapy for ovarian serous cystadenocarcinoma. However, further in-depth studies are indispensable to increase the validity of this study.

Notes

Compliance with Ethical Standards

Funding

This work was supported by a Medical Excellence Award funded by a Creative Research Development Grant from the First Affiliated Hospital of Guangxi Medical University.

Conflict of interest

Dan-dan Xiong, Yue Qin, Wen-qing Xu, Rong-quan He, Hua-yu Wu, Dan-min Wei, Jing-jing Zeng, Yi-wu Dang, and Gang Chen have no conflicts of interest directly relevant to the contents of this article.

Supplementary material

40261_2018_683_MOESM1_ESM.pdf (147 kb)
Supplementary material 1 (PDF 147 kb)

References

  1. 1.
    Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136:E359–86.CrossRefPubMedGoogle Scholar
  2. 2.
    Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin. 2018;68:7–30.CrossRefPubMedGoogle Scholar
  3. 3.
    Kaldawy A, Segev Y, Lavie O, Auslender R, Sopik V, Narod SA. Low-grade serous ovarian cancer: a review. Gynecol Oncol. 2016;143:433–8.CrossRefPubMedGoogle Scholar
  4. 4.
    Li J, Fadare O, Xiang L, Kong B, Zheng W. Ovarian serous carcinoma: recent concepts on its origin and carcinogenesis. J Hematol Oncol. 2012;5:8.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Torre LA, Islami F, Siegel RL, Ward EM, Jemal A. Global cancer in women: burden and trends. Cancer Epidemiol Biomark Prev. 2017;26:444–57.CrossRefGoogle Scholar
  6. 6.
    Bachmayr-Heyda A, Aust S, Auer K, Meier SM, Schmetterer KG, Dekan S, et al. Integrative systemic and local metabolomics with impact on survival in high-grade serous ovarian cancer. Clin Cancer Res. 2017;23:2081–92.CrossRefPubMedGoogle Scholar
  7. 7.
    Bowtell DD, Bohm S, Ahmed AA, Aspuria PJ, Bast RC Jr, Beral V, et al. Rethinking ovarian cancer II: reducing mortality from high-grade serous ovarian cancer. Nat Rev Cancer. 2015;15:668–79.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Jayson GC, Kohn EC, Kitchener HC, Ledermann JA. Ovarian cancer. Lancet. 2014;384:1376–88.CrossRefPubMedGoogle Scholar
  9. 9.
    Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008;4:682–90.CrossRefPubMedGoogle Scholar
  10. 10.
    Kibble M, Saarinen N, Tang J, Wennerberg K, Makela S, Aittokallio T. Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products. Nat Prod Rep. 2015;32:1249–66.CrossRefPubMedGoogle Scholar
  11. 11.
    Li S, Zhang B. Traditional Chinese medicine network pharmacology: theory, methodology and application. Chin J Nat Med. 2013;11:110–20.CrossRefPubMedGoogle Scholar
  12. 12.
    Yu G, Zhang Y, Ren W, Dong L, Li J, Geng Y, et al. Network pharmacology-based identification of key pharmacological pathways of Yin-Huang-Qing-Fei capsule acting on chronic bronchitis. Int J Chron Obstruct Pulmon Dis. 2017;12:85–94.CrossRefPubMedGoogle Scholar
  13. 13.
    Qi Q, Li R, Li HY, Cao YB, Bai M, Fan XJ, et al. Identification of the anti-tumor activity and mechanisms of nuciferine through a network pharmacology approach. Acta Pharmacol Sin. 2016;37:963–72.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Azmi AS. Adopting network pharmacology for cancer drug discovery. Curr Drug Discov Technol. 2013;10:95–105.CrossRefPubMedGoogle Scholar
  15. 15.
    Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45:W98–102.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Pinero J, Bravo A, Queralt-Rosinach N, Gutierrez-Sacristan A, Deu-Pons J, Centeno E, et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 2017;45:D833–9.CrossRefPubMedGoogle Scholar
  17. 17.
    Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284–7.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Wu G, Dawson E, Duong A, Haw R, Stein L. ReactomeFIViz: a Cytoscape app for pathway and network-based data analysis. F1000Res. 2014;3:146.Google Scholar
  19. 19.
    Musa A, Ghoraie LS, Zhang SD, Glazko G, Yli-Harja O, Dehmer M, et al. A review of connectivity map and computational approaches in pharmacogenomics. Brief Bioinform. 2017;18:903.CrossRefPubMedGoogle Scholar
  20. 20.
    Szklarczyk D, Santos A, von Mering C, Jensen LJ, Bork P, Kuhn M. STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res. 2016;44:D380–4.CrossRefPubMedGoogle Scholar
  21. 21.
    Su G, Morris JH, Demchak B, Bader GD. Biological network exploration with Cytoscape 3. Curr Protoc Bioinform. 2014;47:8.13.1–24.Google Scholar
  22. 22.
    Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, et al. PubChem substance and compound databases. Nucleic Acids Res. 2016;44:D1202–13.CrossRefPubMedGoogle Scholar
  23. 23.
    de Anda-Jauregui G, Guo K, McGregor BA, Hur J. Exploration of the anti-inflammatory drug space through network pharmacology: applications for drug eepurposing. Front Physiol. 2018;9:151.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Cao H, Li S, Xie R, Xu N, Qian Y, Chen H, et al. Exploring the mechanism of dangguiliuhuang decoction against hepatic fibrosis by network pharmacology and experimental validation. Front Pharmacol. 2018;9:187.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Liu H, Zeng L, Yang K, Zhang G. A network pharmacology approach to explore the pharmacological mechanism of xiaoyao powder on anovulatory infertility. Evid Based Compl Alternat Med. 2016;2016:2960372.Google Scholar
  26. 26.
    Du J, Yuan Z, Ma Z, Song J, Xie X, Chen Y. KEGG-PATH: Kyoto Encyclopedia of Genes and Genomes-based pathway analysis using a path analysis model. Mol BioSyst. 2014;10:2441–7.CrossRefPubMedGoogle Scholar
  27. 27.
    Fagundes CP, Glaser R, Johnson SL, Andridge RR, Yang EV, Di Gregorio MP, et al. Basal cell carcinoma: stressful life events and the tumor environment. Arch Gen Psychiatry. 2012;69:618–26.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Flecken T, Spangenberg HC, Thimme R. Immunobiology of hepatocellular carcinoma. Langenbecks Arch Surg. 2012;397:673–80.CrossRefPubMedGoogle Scholar
  29. 29.
    Sprinzl MF, Galle PR. Immune control in hepatocellular carcinoma development and progression: role of stromal cells. Semin Liver Dis. 2014;3:376–88.CrossRefGoogle Scholar
  30. 30.
    Kalaria R. Similarities between Alzheimer’s disease and vascular dementia. J Neurol Sci. 2002;203–204:29–34.CrossRefPubMedGoogle Scholar
  31. 31.
    Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabasi AL. The human disease network. Proc Natl Acad Sci USA. 2007;104:8685–90.CrossRefPubMedGoogle Scholar
  32. 32.
    Hu G, Agarwal P. Human disease-drug network based on genomic expression profiles. PLoS One. 2009;4:e6536.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Qu XA, Rajpal DK. Applications of connectivity map in drug discovery and development. Drug Discov Today. 2012;1:1289–98.CrossRefGoogle Scholar
  34. 34.
    Subramanian A, Narayan R, Corsello SM, Peck DD, Natoli TE, Lu X, et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell. 2017;171(1437–52):e17.Google Scholar
  35. 35.
    Malcomson B, Wilson H, Veglia E, Thillaiyampalam G, Barsden R, Donegan S, et al. Connectivity mapping (ssCMap) to predict A20-inducing drugs and their antiinflammatory action in cystic fibrosis. Proc Natl Acad Sci USA. 2016;113:E3725–34.CrossRefPubMedGoogle Scholar
  36. 36.
    Walf-Vorderwulbecke V, Pearce K, Brooks T, Hubank M, van den Heuvel-Eibrink MM, Zwaan CM, et al. Targeting acute myeloid leukemia by drug-induced c-MYB degradation. Leukemia. 2018;32:882–9.CrossRefPubMedGoogle Scholar
  37. 37.
    Chien W, Sun QY, Lee KL, Ding LW, Wuensche P, Torres-Fernandez LA, et al. Activation of protein phosphatase 2A tumor suppressor as potential treatment of pancreatic cancer. Mol Oncol. 2015;9:889–905.CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Sengottuvelan M, Deeptha K, Nalini N. Influence of dietary resveratrol on early and late molecular markers of 1,2-dimethylhydrazine-induced colon carcinogenesis. Nutrition. 2009;25:1169–76.CrossRefPubMedGoogle Scholar
  39. 39.
    Tan L, Wang W, He G, Kuick RD, Gossner G, Kueck AS, et al. Resveratrol inhibits ovarian tumor growth in an in vivo mouse model. Cancer. 2016;122:722–9.CrossRefPubMedGoogle Scholar
  40. 40.
    Piotrowska-Kempisty H, Rucinski M, Borys S, Kucinska M, Kaczmarek M, Zawierucha P, et al. 3′-hydroxy-3,4,5,4′-tetramethoxystilbene, the metabolite of resveratrol analogue DMU-212, inhibits ovarian cancer cell growth in vitro and in a mice xenograft model. Sci Rep. 2016;6:32627.CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Carter LG, D’Orazio JA, Pearson KJ. Resveratrol and cancer: focus on in vivo evidence. Endocr Relat Cancer. 2014;21:R209–25.CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Popat R, Plesner T, Davies F, Cook G, Cook M, Elliott P, et al. A phase 2 study of SRT501 (resveratrol) with bortezomib for patients with relapsed and or refractory multiple myeloma. Br J Haematol. 2013;160:714–7.CrossRefPubMedGoogle Scholar
  43. 43.
    de Jong E, Winkel P, Poelstra K, Prakash J. Anticancer effects of 15d-prostaglandin-J2 in wild-type and doxorubicin-resistant ovarian cancer cells: novel actions on SIRT1 and HDAC. PLoS One. 2011;6:e25192.CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Nagai H, Fujioka-Kobayashi M, Ohe G, Hara K, Takamaru N, Uchida D, et al. Antitumour effect of valproic acid against salivary gland cancer in vitro and in vivo. Oncol Rep. 2014;31:1453–8.CrossRefPubMedGoogle Scholar
  45. 45.
    Mattheolabakis G, Wang R, Rigas B, Mackenzie GG. Phospho-valproic acid inhibits pancreatic cancer growth in mice: enhanced efficacy by its formulation in poly-(l)-lactic acid-poly(ethylene glycol) nanoparticles. Int J Oncol. 2017;51:1035–44.CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Cincarova L, Zdrahal Z, Fajkus J. New perspectives of valproic acid in clinical practice. Expert Opin Investig Drugs. 2013;22:1535–47.CrossRefPubMedGoogle Scholar
  47. 47.
    Falchook GS, Fu S, Naing A, Hong DS, Hu W, Moulder S, et al. Methylation and histone deacetylase inhibition in combination with platinum treatment in patients with advanced malignancies. Invest New Drugs. 2013;31:1192–200.CrossRefPubMedGoogle Scholar
  48. 48.
    Jung JH, Sohn EJ, Shin EA, Lee D, Kim B, Jung DB, et al. Melatonin suppresses the expression of 45S preribosomal RNA and upstream binding factor and enhances the antitumor activity of puromycin in MDA-MB-231 breast cancer cells. Evid Based Complement Alternat Med. 2013;2013:879746.PubMedPubMedCentralGoogle Scholar
  49. 49.
    Singh SV, Ajay AK, Mohammad N, Malvi P, Chaube B, Meena AS, et al. Proteasomal inhibition sensitizes cervical cancer cells to mitomycin C-induced bystander effect: the role of tumor microenvironment. Cell Death Dis. 2015;6:e1934.CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Li W, Zhang X, Olumi AF. MG-132 sensitizes TRAIL-resistant prostate cancer cells by activating c-Fos/c-Jun heterodimers and repressing c-FLIP(L). Cancer Res. 2007;67:2247–55.CrossRefPubMedGoogle Scholar
  51. 51.
    Lu H, Yang XF, Tian XQ, Tang SL, Li LQ, Zhao S, et al. The in vitro and vivo anti-tumor effects and molecular mechanisms of suberoylanilide hydroxamic acid (SAHA) and MG132 on the aggressive phenotypes of gastric cancer cells. Oncotarget. 2016;7:56508–25.PubMedPubMedCentralGoogle Scholar
  52. 52.
    Guo N, Peng Z, Zhang J. Proteasome inhibitor MG132 enhances sensitivity to cisplatin on ovarian carcinoma cells in vitro and in vivo. Int J Gynecol Cancer. 2016;26:839–44.CrossRefPubMedGoogle Scholar
  53. 53.
    Vitale P, Panella A, Scilimati A, Perrone MG. COX-1 inhibitors: beyond structure toward therapy. Med Res Rev. 2016;36:641–71.Google Scholar
  54. 54.
    Lubig J, Lattrich C, Springwald A, Haring J, Schuler S, Ortmann O, et al. Effects of a combined treatment with GPR30 agonist G-1 and herceptin on growth and gene expression of human breast cancer cell lines. Cancer Invest. 2012;30:372–9.CrossRefPubMedGoogle Scholar
  55. 55.
    Hjortso MD, Andersen MH. The expression, function and targeting of haem oxygenase-1 in cancer. Curr Cancer Drug Targets. 2014;14:337–47.CrossRefPubMedGoogle Scholar
  56. 56.
    Yildiz-Ozer M, Oztopcu-Vatan P, Kus G. The investigation of ceranib-2 on apoptosis and drug interaction with carboplatin in human non small cell lung cancer cells in vitro. Cytotechnology. 2018;70:387–96.CrossRefPubMedGoogle Scholar
  57. 57.
    Ivan C, Hu W, Bottsford-Miller J, Zand B, Dalton HJ, Liu T, et al. Epigenetic analysis of the Notch superfamily in high-grade serous ovarian cancer. Gynecol Oncol. 2013;128:506–11.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of PathologyFirst Affiliated Hospital of Guangxi Medical UniversityNanningChina
  2. 2.College of Pharmaceutical ScienceGuangxi Medical UniversityNanningChina
  3. 3.Department of Medical OncologyFirst Affiliated Hospital of Guangxi Medical UniversityNanningChina
  4. 4.Department of Cell Biology and Genetics, School of Preclinical MedicineGuangxi Medical UniversityNanningChina

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