Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

MicroRNA-654-5p suppresses ovarian cancer development impacting on MYC, WNT and AKT pathways

Abstract

Ovarian cancer is the most lethal gynecological malignancy due to the silent nature on its early onset and the rapid acquisition of drug resistance. Histologically heterogeneous, it includes several subtypes with different mutational landscapes, hampering the development of effective targeted therapies. Non-coding RNAs are emerging as potential new therapeutic targets in cancer. To search for a microRNA signature related to ovarian carcinomas and study its potential as effective targeted therapy, we examined the expression of 768 miRNA in a large collection of tumor samples and found miR-654-5p to be infraexpressed in ovarian serous carcinomas, the most common and aggressive type. Restoration of miR-654-5p levels reduced tumor cell viability in vitro and in vivo and impaired sphere formation capacity and viability of ovarian cancer patient-derived ascitic cells ex vivo. CDCP1 and PLAGL2 oncogenes were found to be the most relevant direct miR-654-5p targets and both genes convey in a molecular signature associated with key cancer pathways relevant to ovarian tumorigenesis, such as MYC, WNT and AKT pathways. Together, we unveiled the tumor suppressor function of miR-654-5p, suggesting that its restoration or co-targeting of CDCP1 and PLAGL2 may be an effective therapeutic approach for ovarian cancer.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA. 2017;67:7–30.

    PubMed  Google Scholar 

  2. Karnezis AN, Cho KR, Gilks CB, Pearce CL, Huntsman DG. The disparate origins of ovarian cancers: pathogenesis and prevention strategies. Nat Rev Cancer. 2016;17:65–74.

    Article  Google Scholar 

  3. Matulonis UA, Sood AK, Fallowfield L, Howitt BE, Sehouli J, Karlan BY. Ovarian cancer. Nat Rev Dis Prim. 2016;2:16061.

    Article  Google Scholar 

  4. Ueland F. A perspective on ovarian cancer biomarkers: past, present and yet-to-come. Diagnostics. 2017;7:14.

    Article  Google Scholar 

  5. Kipps E, Tan DSP, Kaye SB. Meeting the challenge of ascites in ovarian cancer: new avenues for therapy and research. Nat Rev Cancer. 2013;13:273–82.

    Article  CAS  Google Scholar 

  6. Vaughan S, Coward JI, Bast RC, Berchuck A, Berek JS, Brenton JD, et al. Rethinking ovarian cancer: recommendations for improving outcomes. Nat Rev Cancer. 2011;11:719–25.

    Article  CAS  Google Scholar 

  7. Banerjee S, Kaye SB. New strategies in the treatment of ovarian cancer: current clinical perspectives and future potential. Clin Cancer Res. 2013;19:961–8.

    Article  CAS  Google Scholar 

  8. Sehouli J, Braicu E, Chekerov R. PARP inhibitors for recurrent ovarian carcinoma: current treatment options and future perspectives. Geburtshilfe Frau. 2016;76:164–9.

    Article  CAS  Google Scholar 

  9. George A, Kaye S, Banerjee S. Delivering widespread BRCA testing and PARP inhibition to patients with ovarian cancer. Nat Rev Clin Oncol. 2016. https://doi.org/10.1038/nrclinonc.2016.191.

    Article  Google Scholar 

  10. Rupaimoole R, Slack FJ. MicroRNA therapeutics: towards a new era for the management of cancer and other diseases. Nat Rev Drug Disco. 2017;16:203–22.

    Article  CAS  Google Scholar 

  11. Ji W, Sun B, Su C. Targeting microRNAs in cancer gene therapy. Genes. 2017;8:21.

    Article  Google Scholar 

  12. Prahm KP, Novotny GW, Høgdall C, Høgdall E. Current status on microRNAs as biomarkers for ovarian cancer. APMIS. 2016;124:337–55.

    Article  Google Scholar 

  13. Zhang L, Volinia S, Bonome T, Calin GA, Greshock J, Yang N, et al. Genomic and epigenetic alterations deregulate microRNA expression in human epithelial ovarian cancer. Proc Natl Acad Sci. 2008;105:7004–9.

    Article  CAS  Google Scholar 

  14. Yang D, Sun Y, Hu L, Zheng H, Ji P, Pecot CV, et al. Integrated analyses identify a master microRNA regulatory network for the mesenchymal subtype in serous ovarian cancer. Cancer Cell. 2013;23:186–99.

    Article  CAS  Google Scholar 

  15. Mateescu B, Batista L, Cardon M, Gruosso T, de Feraudy Y, Mariani O, et al. MiR-141 and miR-200a act on ovarian tumorigenesis by controlling oxidative stress response. Nat Med. 2011;17:1627–35.

    Article  CAS  Google Scholar 

  16. Bell D, Berchuck A, Birrer M, Chien J, Cramer DW, Dao F, et al. Integrated genomic analyses of ovarian carcinoma. Nature. 2011;474:609–15.

    Article  CAS  Google Scholar 

  17. Beg MS, Brenner AJ, Sachdev J, Borad M, Kang Y-K, Stoudemire J, et al. Phase I study of MRX34, a liposomal miR-34a mimic, administered twice weekly in patients with advanced solid tumors. Invest New Drugs. 2017;35:180–8.

    Article  CAS  Google Scholar 

  18. Zehavi L, Avraham R, Barzilai A, Bar-Ilan D, Navon R, Sidi Y, et al. Silencing of a large microRNA cluster on human chromosome 14q32 in melanoma: biological effects of mir-376a and mir-376c on insulin growth factor 1 receptor. Mol Cancer. 2012;11:44.

    Article  CAS  Google Scholar 

  19. Maire G, Martin JW, Yoshimoto M, Chilton-MacNeill S, Zielenska M, Squire JA. Analysis of miRNA-gene expression-genomic profiles reveals complex mechanisms of microRNA deregulation in osteosarcoma. Cancer Genet. 2011;204:138–46.

    Article  CAS  Google Scholar 

  20. Takahashi M, Tsukamoto Y, Kai T, Tokunaga A, Nakada C, Hijiya N, et al. Downregulation of WDR20 due to loss of 14q is involved in the malignant transformation of clear cell renal cell carcinoma. Cancer Sci. 2016;107:417–23.

    Article  CAS  Google Scholar 

  21. Li J, Zhou D, Wang Z, Tan L, Zhou Y, Li J, et al. Reversal effect of 5-aza-2-deoxycytidine on the maternally expressed gene 3 promoter hypermethylation and its inhibitory effect on the proliferation of epithelial ovarian cancer cells. Zhonghua Zhong Liu Za Zhi. 2015;37:324–9.

    CAS  PubMed  Google Scholar 

  22. Adair SJ, Hogan KT. Treatment of ovarian cancer cell lines with 5-aza-2′-deoxycytidine upregulates the expression of cancer-testis antigens and class I major histocompatibility complex-encoded molecules. Cancer Immunol Immunother. 2009;58:589–601.

    Article  CAS  Google Scholar 

  23. Jacob F, Hitchins MP, Fedier A, Brennan K, Nixdorf S, Hacker NF, et al. Expression of GBGT1 is epigenetically regulated by DNA methylation in ovarian cancer cells. BMC Mol Biol. 2014;15:24.

    Article  Google Scholar 

  24. Chen M-W, Yang S-T, Chien M-H, Hua K-T, Wu C-J, Hsiao SM, et al. The STAT3-miRNA-92-Wnt signaling pathway regulates spheroid formation and malignant progression in ovarian cancer. Cancer Res. 2017;77:1955–67.

    Article  CAS  Google Scholar 

  25. Honkoop AH, Pinedo HM, De Jong JS, Verheul HM, Linn SC, Hoekman K, et al. Effects of chemotherapy on pathologic and biologic characteristics of locally advanced breast cancer. Am J Clin Pathol. 1997;107:211–8.

    Article  CAS  Google Scholar 

  26. Dweep H, Gretz N. MiRWalk2.0: a comprehensive atlas of microRNA-target interactions. Nat Methods. 2015;12:697.

    Article  CAS  Google Scholar 

  27. Sato E, Olson SH, Ahn J, Bundy B, Nishikawa H, Qian F, et al. Intraepithelial CD8 + tumor-infiltrating lymphocytes and a high CD8 + /regulatory T cell ratio are associated with favorable prognosis in ovarian cancer. Proc Natl Acad Sci. 2005;102:18538–43.

    Article  CAS  Google Scholar 

  28. Bi L, Yang Q, Yuan J, Miao Q, Duan L, Li F, et al. MicroRNA-127-3p acts as a tumor suppressor in epithelial ovarian cancer by regulating the BAG5 gene. Oncol Rep. 2016. https://doi.org/10.3892/or.2016.5055.

    Article  PubMed  Google Scholar 

  29. Shepherd TG, Thériault BL, Campbell EJ, Nachtigal MW. Primary culture of ovarian surface epithelial cells and ascites-derived ovarian cancer cells from patients. Nat Protoc. 2007;1:2643–9.

    Article  Google Scholar 

  30. Cummins JM, He Y, Leary RJ, Pagliarini R, Diaz LA, Sjoblom T, et al. The colorectal microRNAome. Proc Natl Acad Sci USA. 2006;103:3687–92.

    Article  CAS  Google Scholar 

  31. Paydas S, Acikalin A, Ergin M, Celik H, Yavuz B, Tanriverdi K. Micro-RNA (miRNA) profile in Hodgkin lymphoma: association between clinical and pathological variables. Med Oncol. 2016;33:34.

    Article  Google Scholar 

  32. Tan Y-Y, Xu X-Y, Wang J-F, Zhang C-W, Zhang S-C. MiR-654-5p attenuates breast cancer progression by targeting EPSTI1. Am J Cancer Res. 2016;6:522–32.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Kircher M, Bock C, Paulsen M. Structural conservation versus functional divergence of maternally expressed microRNAs in the Dlk1/Gtl2 imprinting region. BMC Genom. 2008;9:346.

    Article  Google Scholar 

  34. Ostling P, Leivonen S-K, Aakula A, Kohonen P, Makela R, Hagman Z, et al. Systematic analysis of microRNAs targeting the androgen receptor in prostate cancer cells. Cancer Res. 2011;71:1956–67.

    Article  Google Scholar 

  35. Landrette SF, Kuo YH, Hensen K, Barjesteh van Waalwijk van Doorn-Khosrovani S, Perrat PN, Van de Ven WJ, et al. Plag1 and Plagl2 are oncogenes that induce acute myeloid leukemia in cooperation with Cbfb-MYH11. Blood. 2005;105:2900–7.

    Article  CAS  Google Scholar 

  36. He Y, Wu AC, Harrington BS, Davies CM, Wallace SJ, Adams MN, et al. Elevated CDCP1 predicts poor patient outcome and mediates ovarian clear cell carcinoma by promoting tumor spheroid formation, cell migration and chemoresistance. Oncogene. 2016;35:468–78.

    Article  CAS  Google Scholar 

  37. Harrington BS, He Y, Davies CM, Wallace SJ, Adams MN, Beaven EA, et al. Cell line and patient-derived xenograft models reveal elevated CDCP1 as a target in high-grade serous ovarian cancer. Br J Cancer. 2016;114:417–26.

    Article  CAS  Google Scholar 

  38. Hensen K, Van Valckenborgh ICC, Kas K, Van de Ven WJM, Voz ML. The tumorigenic diversity of the three PLAG family members is associated with different DNA binding capacities. Cancer Res. 2002;62:1510–7.

    CAS  PubMed  Google Scholar 

  39. Landrette SF, Madera D, He F, Castilla LH. The transcription factor PlagL2 activates Mpl transcription and signaling in hematopoietic progenitor and leukemia cells. Leukemia. 2011;25:655–62.

    Article  CAS  Google Scholar 

  40. Poole CJ, van Riggelen J. MYC-Master regulator of the cancer epigenome and transcriptome. Genes. 2017. https://doi.org/10.3390/genes8050142.

    Article  Google Scholar 

  41. Reyes-Gonzalez JM, Armaiz-Pena GN, Mangala LS, Valiyeva F, Ivan C, Pradeep S, et al. Targeting c-MYC in platinum-resistant ovarian cancer. Mol Cancer Ther. 2015;14:2260–9.

    Article  CAS  Google Scholar 

  42. Zheng H, Ying H, Wiedemeyer R, Yan H, Quayle SN, Ivanova EV, et al. PLAGL2 regulates Wnt signaling to impede differentiation in neural stem cells and gliomas. Cancer Cell. 2010;17:497–509.

    Article  CAS  Google Scholar 

  43. Wang Y-P, Guo P-T, Zhu Z, Zhang H, Xu Y, Chen Y-Z et al. Pleomorphic adenoma gene like-2 induces epithelial-mesenchymal transition via Wnt/b-catenin signaling pathway in human colorectal adenocarcinoma. Oncol Rep. 2017. https://doi.org/10.3892/or.2017.5485.

    Article  CAS  Google Scholar 

  44. Livak KJST. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods. 2001;25:402–8.

    Article  CAS  Google Scholar 

  45. Klinkebiel D, Zhang W, Akers SN, Odunsi K, Karpf AR. DNA methylome analyses implicate fallopian tube epithelia as the origin for high-grade serous ovarian cancer. Mol Cancer Res. 2016;14:787–94.

    Article  CAS  Google Scholar 

  46. Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, et al. A landscape of pharmacogenomic interactions in cancer. Cell. 2016;166:740–54.

    Article  CAS  Google Scholar 

  47. Moran S, Vizoso M, Martinez-Cardus A, Gomez A, Matias-Guiu X, Chiavenna SM, et al. Validation of DNA methylation profiling in formalin-fixed paraffin-embedded samples using the infinium humanmethylation450 microarray. Epigenetics. 2014;9:829–33.

    Article  Google Scholar 

  48. Jubierre L, Soriano A, Planells-Ferrer L, París-Coderch L, Tenbaum SP, Romero OA, et al. BRG1/SMARCA4 is essential for neuroblastoma cell viability through modulation of cell death and survival pathways. Oncogene. 2016;35:5179–90.

    Article  CAS  Google Scholar 

  49. Soriano A, París-Coderch L, Jubierre L, Martínez A, Zhou X, Piskareva O, et al. MicroRNA-497 impairs the growth of chemoresistant neuroblastoma cells by targeting cell cycle, survival and vascular permeability genes. Oncotarget. 2016;7:9271–87.

    Article  Google Scholar 

Download references

Acknowledgements

We are thankful to Drs. Diego Arango and Agueda Martinez, Dr. Sung Goo Park and Dr. Rosanna Paciucci for 9E10 c-Myc, anti-HAX1 and anti-RB and anti-p-RB antibodies, respectively. We thank Dr. Francesc Viñals, Dr. Barbara Vanderhyden, Dr. Antonio Rosato, Dr. Erich A. Nigg and the Ovarian Cancer Research Team for cell lines. We are grateful to Drs. Aroa Soriano and Luz Jubierre for experimental help. We acknowledge technical support from the Flow Cytometry Facility at Center of Genomic Regulation (CRG) and the Unitat d’Estadística i Bioinformàtica (UEB) and the Unitat d’Alta Tecnologia (UAT) at VHIR. We thank all our lab members for support and helpful discussions. This work was supported in part by grants from Instituto de la Mujer Dexeus (DEXEUS-B29/012), CIBER (CB16/12/00328), SGR (2017 SGR 1661), the Ministerio de Economia y Competitividad and Fondos FEDER (RTC-2015-3821-1), Instituto Carlos III (PI15/00238 to A.S. and PI17/00564 to M.F.S) and the Miguel Servet Program (CP13/00158 and CPII18/00027 to AS. and CPII16/00006 to MFS). AP and LS were supported by predoctoral VHIR fellowships and CJ by an AGAUR predoctoral fellowship (VHIR: PRED-VHIR-2014-11 and PRED-VHIR-2017; AGAUR: 2017FI_B_00095, respectively).

Author information

Authors and Affiliations

Authors

Contributions

BM and AS conceived and designed the study with help of AP and MFS. BM performed the miRNA and mRNA microarray analyses, and CJ carried out the mRNA microarray statistics and bioinformatics. BM and AP carried out all experiments and data analyses with the help of LS and MB. AM and GT provided technical support. ME and SM performed the DNA methylation analyses. JC analyzed and interpreted the IHC. JLS, APB, and AGM coordinated samples collection at HUVH and XMG, GM, FA, JP, and JAL provided with OC samples for the multicenter study. JR and MR initiated the miRNA screening. BM and AS wrote the manuscript with input from all other authors. AS supervised the project.

Corresponding author

Correspondence to Anna Santamaría.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Majem, B., Parrilla, A., Jiménez, C. et al. MicroRNA-654-5p suppresses ovarian cancer development impacting on MYC, WNT and AKT pathways. Oncogene 38, 6035–6050 (2019). https://doi.org/10.1038/s41388-019-0860-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41388-019-0860-0

This article is cited by

Search

Quick links