Skip to main content

Advertisement

SpringerLink
Log in

Ascites-Derived Extracellular microRNAs as Potential Biomarkers for Ovarian Cancer

  • Original Article
  • Published:
Reproductive Sciences Aims and scope Submit manuscript

Abstract

Ovarian cancer as the most fatal gynecological malignancy is often manifested by excessive fluid accumulation known as ascites or effusion. Ascites-derived microRNAs (miRNAs) may be closely associated with ovarian cancer progression. However, our knowledge of their roles, altered expression, and clinical outcomes remained limited. In this study, large-scale expression profiling of 754 human miRNAs was performed using real-time quantitative polymerase chain reaction and 384-well TaqMan array human miRNA A and B cards to identify differentially expressed miRNAs between extracellular fraction of the ascitic fluid associated with high-grade serous ovarian carcinomas and control plasma. Of the 754 miRNAs, 153 were significantly differentially expressed relative to the controls. Expression of 7 individual miRNAs (miR-200a, miR-200b, miR-200c, miR-141, miR-429, miR-1290, and miR-30a-5p) was further validated in extended sample sets, including serous, endometrioid, and mucinous subtypes. All miR-200 family members and miR-1290 were conspicuously overexpressed, while miR-30a-5p was only weakly overexpressed. The ability of miRNAs expression to discriminate the pathological samples from the controls was strong. Receiver operating characteristic curve analyses found area under the curve (AUC) values of 1.000 for miR-200a, miR-200c, miR-141, miR-429, and miR-1290 and of AUC 0.996 and 0.885 for miR-200b and miR-30a-5p, respectively. Preliminary survival analyses indicated low expression level of miR-200b as significantly related to longer overall survival (hazard ratio [HR]: 0.25, mean survival 44 months), while high expression level was related to poor overall survival (HR: 4.04, mean survival 24 months). Our findings suggested that ascites-derived miRNAs should be further explored and evaluated as potential diagnostic and prognostic biomarkers for ovarian cancer.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Matz M, Coleman MP, Carreira H, Salmeron D, Chirlaque MD, Allemani C. Worldwide comparison of ovarian cancer survival: histological group and stage at diagnosis (CONCORD-2). Gynecol Oncol. 2017;144(2):396–404.

    PubMed  Google Scholar 

  2. Wei W, Li N, Sun Y, Li B, Xu L, Wu L. Clinical outcome and prognostic factors of patients with early stage epithelial ovarian cancer. Oncotarget. 2017;8(14):23862–23870.

    PubMed  Google Scholar 

  3. Puls LE, Duniho T, Hunter JE, Kryscio R, Blackhurst D, Gallion H. The prognostic implication of ascites in advanced-stage ovarian cancer. Gynecol Oncol. 1996;61(1):109–112.

    CAS  PubMed  Google Scholar 

  4. Ahmed N, Stenvers KL. Getting to know ovarian cancer ascites: opportunities for targeted therapy-based translational research. Front Oncol. 2013;3:256.

    PubMed  PubMed Central  Google Scholar 

  5. Shender VO, Pavlyukov MS, Ziganshin RH, et al. Proteome-metabolome profiling of ovarian cancer ascites reveals novel components involved in intercellular communication. Mol Cell Proteomics. 2014;13(12):3558–3571.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. van Jaarsveld MT, Helleman J, Berns EM, Wiemer EA. MicroRNAs in ovarian cancer biology and therapy resistance. Int J Biochem Cell Biol. 2010;42(8):1282–1290.

    PubMed  Google Scholar 

  7. Iorio MV, Croce CM. MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review. EMBO Mol Med. 2012;4(3):143–159.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Zuberi M, Khan I, Gandhi G, Ray PC, Saxena A. The conglomeration of diagnostic, prognostic and therapeutic potential of serum miR-199a and its association with clinicopathological features in epithelial ovarian cancer. Tumor Biol. 2016;37(8):11259–11266.

    CAS  Google Scholar 

  9. Rupaimoole R, Slack FJ. MicroRNA therapeutics: towards a new era for the management of cancer and other diseases. Nat Rev Drug Discovery. 2017;16(3):203–221.

    CAS  PubMed  Google Scholar 

  10. Vaksman O, Stavnes HT, Kaern J, Trope CG, Davidson B, Reich R. miRNA profiling along tumour progression in ovarian carcinoma. J Cell Mol Med. 2011;15(7):1593–1602.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Nymoen DA, Slipicevic A, Holth A, et al. MiR-29a is a candidate biomarker of better survival in metastatic high-grade serous carcinoma. Hum Pathol. 2016;54:74–81.

    CAS  PubMed  Google Scholar 

  12. Chung YW, Bae HS, Song JY, et al. Detection of microRNA as novel biomarkers of epithelial ovarian cancer from the serum of ovarian cancer patient. Int J Gynecol Cancer. 2013;23(4):673–679.

    PubMed  Google Scholar 

  13. Cappellesso R, Tinazzi A, Giurici T, et al. Programmed cell death 4 and microRNA 21 inverse expression is maintained in cells and exosomes from ovarian serous carcinoma effusions. Cancer Cytopathol. 2014;122(9):685–693.

    CAS  PubMed  Google Scholar 

  14. Vaksman O, Tropę C, Davidson B, Reich R. Exosome-derived miRNAs and ovarian carcinoma progression. Carcinogenesis. 2014;35(9):2113–2120.

    CAS  PubMed  Google Scholar 

  15. Vandesompele J, De Preter K, Pattyn F, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002;3(7):0034.1.

    Google Scholar 

  16. Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: Bestkeeper — Excel-based tool using pair-wise correlations. Biotechnol Lett. 2004;26(6):509–515.

    CAS  PubMed  Google Scholar 

  17. Hellemans J, Mortier G, De Paepe A, Speleman F, Vandesompele J. qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol. 2007;8(2): R19.

    PubMed  PubMed Central  Google Scholar 

  18. Makarova JA, Shkurnikov MU, Wicklein D, et al. Intracellular and extracellular microRNA: an update on localization and biological role. Prog Histochem Cytochem. 2016;51(3–4):33–49.

    PubMed  Google Scholar 

  19. Weber JA, Baxter DH, Zhang SL, et al. The microRNA spectrum in 12 body fluids. Clin Chem. 2010;56(11):1733–1741.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Kosaka N, Iguchi H, Ochiya T. Circulating microRNA in body fluid: a new potential biomarker for cancer diagnosis and prognosis. Cancer Sci. 2010;101(10):2087–2092.

    CAS  PubMed  Google Scholar 

  21. Witwer KW. Circulating microRNA biomarker studies: pitfalls and potential solutions. Clin Chem. 2015;61(1):56–63.

    CAS  PubMed  Google Scholar 

  22. Turchinovich A, Weiz L, Burwinkel B. Extracellular miRNAs: the mystery of their origin and function. Trends Biochem Sci. 2012;37(11):460–465.

    CAS  PubMed  Google Scholar 

  23. Witwer KW, Buzas EI, Bemis LT, et al. Standardization of sample collection, isolation and analysis methods in extracellular vesicle research. J Extracell Vesicles. 2013;2(1):20360.

    Google Scholar 

  24. Blagden SP. Harnessing pandemonium: the clinical implications of tumor heterogeneity in ovarian cancer. Front Oncol. 2015;5:149.

    PubMed  PubMed Central  Google Scholar 

  25. Nelson BH. New insights into tumor immunity revealed by the unique genetic and genomic aspects of ovarian cancer. Curr Opin Immunol. 2015;33:93–100.

    CAS  PubMed  Google Scholar 

  26. Zavesky L, Jandakova E, Turyna R, et al. New perspectives in diagnosis of gynaecological cancers: emerging role of circulating microRNAs as novel biomarkers. Neoplasma. 2015;62(4):509–520.

    CAS  PubMed  Google Scholar 

  27. Lengyel E. Ovarian cancer development and metastasis. Am J Pathol. 2010;177:1053–1064.

    PubMed  PubMed Central  Google Scholar 

  28. Jabbari N, Reavis AN, McDonald JF. Sequence variation among members of the miR-200 microRNA family is correlated with variation in the ability to induce hallmarks of mesenchymal—epithelial transition in ovarian cancer cells. J Ovarian Res. 2014;7:12.

    PubMed  PubMed Central  Google Scholar 

  29. Xu S, Xu P, Wu W, et al. The biphasic expression pattern of miR-200a and E-cadherin in epithelial ovarian cancer and its correlation with clinicopathological features. Curr Pharm Des. 2014;20(11):1888–1895.

    CAS  PubMed  Google Scholar 

  30. Choi PW, Ng SW. The functions of microRNA-200 family in ovarian cancer: beyond epithelial—mesenchymal transition. Int J Mol Sci. 2017;18(6):1207.

    PubMed Central  Google Scholar 

  31. Kan CWS, Hahn MA, Gard GB, et al. Elevated levels of circulating microRNA-200 family members correlate with serous epithelial ovarian cancer. BMC Cancer. 2012;12:627.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Gao YC, Wu J. MicroRNA-200c and microRNA-141 as potential diagnostic and prognostic biomarkers for ovarian cancer. Tumor Biol. 2015;36(6):4843–4850.

    CAS  Google Scholar 

  33. Meng X, Joosse SA, Muller V, et al. Diagnostic and prognostic potential of serum miR-7, miR-16, miR-25, miR-93, miR-182, miR-376a and miR-429 in ovarian cancer patients. Br J Cancer. 2015;113(9):1358–1366.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Shapira I, Oswald M, Lovecchio J, et al. Circulating biomarkers for detection of ovarian cancer and predicting cancer outcomes. Br J Cancer. 2014;110(4):976–983.

    CAS  PubMed  Google Scholar 

  35. Torres A, Torres K, Pesci A, et al. Diagnostic and prognostic significance of miRNA signatures in tissues and plasma of endometrioid endometrial carcinoma patients. Int J Cancer. 2013;132(7):1633–1645.

    CAS  PubMed  Google Scholar 

  36. Wu J, Ji X, Zhu L, et al. Up-regulation of microRNA-1290 impairs cytokinesis and affects the reprogramming of colon cancer cells. Cancer Lett. 2013;329(2):155–163.

    CAS  PubMed  Google Scholar 

  37. Mao Y, Liu J, Zhang D, Li B. MiR-1290 promotes cancer progression by targeting nuclear factor I/X (NFIX) in esophageal squamous cell carcinoma (ESCC). Biomed Pharmacother. 2015;76:82–93.

    CAS  PubMed  Google Scholar 

  38. Kim G, An HJ, Lee MJ, et al. Hsa-miR-1246 and hsa-miR-1290 are associated with stemness and invasiveness of non-small cell lung cancer. Lung Cancer. 2016;91:15–22.

    PubMed  Google Scholar 

  39. Marchini S, Cavalieri D, Fruscio R, et al. Association between miR-200c and the survival of patients with stage I epithelial ovarian cancer: a retrospective study of two independent tumour tissue collections. Lancet Oncol. 2011;12(3):273–285.

    CAS  PubMed  Google Scholar 

  40. Sestito R, Cianfrocca R, Rosano L, et al. miR-30a inhibits endothelin A receptor and chemoresistance in ovarian carcinoma. Oncotarget. 2016;7(4):4009–4023.

    PubMed  Google Scholar 

  41. Chen N, Chon HS, Xiong Y, et al. Human cancer cell line microRNAs associated with in vitro sensitivity to paclitaxel. Oncol Rep. 2014;31(1):376–383.

    CAS  PubMed  Google Scholar 

  42. Zhou J, Gong G, Tan H, et al. Urinary microRNA-30a-5p is a potential biomarker for ovarian serous adenocarcinoma. Oncol Rep. 2015;33(6):2915–2923.

    CAS  PubMed  Google Scholar 

  43. Chung YH, Li SC, Kao YH, et al. MiR-30a-5p inhibits epithelial-to-mesenchymal transition and upregulates expression of tight junction protein Claudin-5 in human upper tract urothelial carcinoma cells. Int J Mol Sci. 2017;18(8):1826.

    PubMed Central  Google Scholar 

  44. Li Y, Li Y, Chen D, et al. MiR-30a-5p in the tumorigenesis of renal cell carcinoma: a tumor suppressive microRNA. Mol Med Rep. 2016;13(5):4085–4094.

    CAS  PubMed  Google Scholar 

  45. Zhang SL, Liu Q, Zhang Q, Liu L. MicroRNA-30a-5p suppresses proliferation, invasion and tumor growth of hepatocellular cancer cells via targeting FOXA1. Oncol Lett. 2017;14(4):5018–5026.

    PubMed  PubMed Central  Google Scholar 

  46. Zhang BW, Cai HF, Wei XF, et al. MiR-30-5p regulates muscle differentiation and alternative splicing of muscle-related genes by targeting MBNL. Int J Mol Sci. 2016;17(2):E182.

    PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. First Faculty of Medicine, Institute of Biology and Medical Genetics, Charles University in Prague and General University Hospital in Prague, Albertov 4, 12800, Prague, Czech Republic

    Luděk Záveský RNDr, PhD, Iveta Svobodová MSc & Aleš Hořínek Ing.

  2. Institute of Pathology, University Hospital Brno, Brno, Czech Republic

    Eva Jandáková MD

  3. Department of Obstetrics and Gynecology, University Hospital Brno and Masaryk University in Brno, Brno, Czech Republic

    Vít Weinberger MD, PhD & Luboš Minář MD, PhD

  4. Faculty Transfusion Centre, General University Hospital in Prague, Prague, Czech Republic

    Veronika Hanzíková MD & Daniela Dušková MD, PhD

  5. Institute of Experimental Botany, Czech Academy of Sciences, Prague, Czech Republic

    Lenka Záveská Drábková RNDr, PhD

Authors
  1. Luděk Záveský RNDr, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eva Jandáková MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vít Weinberger MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luboš Minář MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Veronika Hanzíková MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Daniela Dušková MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lenka Záveská Drábková RNDr, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Iveta Svobodová MSc
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Aleš Hořínek Ing.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luděk Záveský RNDr, PhD.

Additional information

Authors’ Note

All patients provided an informed consent. The study was approved by a multicentric ethics committee of the General University Hospital in Prague.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Záveský, L., Jandáková, E., Weinberger, V. et al. Ascites-Derived Extracellular microRNAs as Potential Biomarkers for Ovarian Cancer. Reprod. Sci. 26, 510–522 (2019). https://doi.org/10.1177/1933719118776808

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1177/1933719118776808

Keywords

Search

Navigation