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Identification of a four-gene methylation biomarker panel in high-grade serous ovarian carcinoma

  • Ivana Baranova EMAIL logo , Helena Kovarikova , Jan Laco , Iva Sedlakova , Filip Vrbacky , Dalibor Kovarik , Petr Hejna , Vladimir Palicka and Marcela Chmelarova
Published/Copyright: March 7, 2020
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Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 58 Issue 8

Abstract

Background

The lack of effective biomarkers for the screening and early detection of ovarian cancer (OC) is one of the most pressing problems in oncogynecology. Because epigenetic alterations occur early in the cancer development, they provide great potential to serve as such biomarkers. In our study, we investigated a potential of a four-gene methylation panel (including CDH13, HNF1B, PCDH17 and GATA4 genes) for the early detection of high-grade serous ovarian carcinoma (HGSOC).

Methods

For methylation detection we used methylation sensitive high-resolution melting analysis and real-time methylation specific analysis. We also investigated the relation between gene hypermethylation and gene relative expression using the 2−ΔΔCt method.

Results

The sensitivity of the examined panel reached 88.5%. We were able to detect methylation in 85.7% (12/14) of early stage tumors and in 89.4% (42/47) of late stage tumors. The total efficiency of the panel was 94.4% and negative predictive value reached 90.0%. The specificity and positive predictive value achieved 100% rates. Our results showed lower gene expression in the tumor samples in comparison to control samples. The more pronounced downregulation was measured in the group of samples with detected methylation.

Conclusions

In our study we designed the four-gene panel for HGSOC detection in ovarian tissue with 100% specificity and sensitivity of 88.5%. The next challenge is translation of the findings to the less invasive source for biomarker examination, such as plasma. Our results indicate that combination of examined genes deserve consideration for further testing in clinical molecular diagnosis of HGSOC.

Keywords: biomarker; DNA methylation; high-grade serous ovarian cancer
Corresponding author: Ivana Baranova, Institute of Clinical Biochemistry and Diagnostics, Charles University Faculty of Medicine and University Hospital Hradec Kralove, Sokolska 581, Hradec Kralove 500 05, Czech Republic, Phone: +420495833864

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This study was supported by Ministry of Health, Czech Republic – conceptual development of research organization (UHHK, 00179906), by the programme PROGRES Q40/11 and SVV 260398, and by European Regional Development Fund-Project BBMRI-CZ: Biobank network – a versatile platform for the research of the etiopathogenesis of diseases, No: EF16 013/0001674.

  3. Employment or leadership: None declared.

  4. Honorarium: None declared.

  5. Competing interests: The funding organizations played no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

References

1. Ferlay J, Colombet M, Soerjomataram I, Dyba T, Randi G, Bettio M, et al. Cancer incidence and mortality patterns in Europe: estimates for 40 countries and 25 major cancers in 2018. Eur J Cancer 2018;103:356–87.10.1016/j.ejca.2018.07.005Search in Google Scholar PubMed

2. Bowtell DD, Bohm S, Ahmed AA, Aspuria PJ, Bast Jr RC, Beral V, et al. Rethinking ovarian cancer II: reducing mortality from high-grade serous ovarian cancer. Nat Rev Cancer 2015;15:668–79.10.1038/nrc4019Search in Google Scholar PubMed PubMed Central

3. Prat J. Ovarian carcinomas: five distinct diseases with different origins, genetic alterations, and clinicopathological features. Virchows Arch 2012;460:237–49.10.1007/s00428-012-1203-5Search in Google Scholar PubMed

4. Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 2011;474:609–15.10.1038/nature10166Search in Google Scholar PubMed PubMed Central

5. Barton CA, Hacker NF, Clark SJ, O’Brien PM. DNA methylation changes in ovarian cancer: Implications for early diagnosis, prognosis and treatment. Gynecol Oncol 2008;109:129–39.10.1016/j.ygyno.2007.12.017Search in Google Scholar PubMed

6. Koukoura O, Spandidos DA, Daponte A, Sifakis S. DNA methylation profiles in ovarian cancer: implication in diagnosis and therapy (Review). Mol Med Rep 2014;10:3–9.10.3892/mmr.2014.2221Search in Google Scholar PubMed PubMed Central

7. Zhang Q, Burdette JE, Wang J-P. Integrative network analysis of TCGA data for ovarian cancer. BMC Systems Biol 2014;8:1–18.10.1186/s12918-014-0136-9Search in Google Scholar PubMed PubMed Central

8. Huang RL, Gu F, Kirma NB, Ruan J, Chen CL, Wang HC, et al. Comprehensive methylome analysis of ovarian tumors reveals hedgehog signaling pathway regulators as prognostic DNA methylation biomarkers. Epigenetics 2013;8:624–34.10.4161/epi.24816Search in Google Scholar PubMed PubMed Central

9. Koch A, Joosten SC, Feng Z, de Ruijter TC, Draht MX, Melotte V, et al. Analysis of DNA methylation in cancer: location revisited. Nat Rev Clin Oncol 2018;15:459–66.10.1038/s41571-018-0004-4Search in Google Scholar PubMed

10. Chmelarova M, Baranova I, Ruszova E, Laco J, Hrochova K, Dvorakova E, et al. Importance of cadherins methylation in ovarian cancer: a next generation sequencing approach. Pathol Oncol Res 2019;25:1457–65.10.1007/s12253-018-0500-ySearch in Google Scholar PubMed

11. Baranova I, Kovarikova H, Laco J, Dvorak O, Sedlakova I, Palicka V, et al. Aberrant methylation of PCDH17 gene in high-grade serous ovarian carcinoma. Cancer Biomark 2018;23:125–33.10.3233/CBM-181493Search in Google Scholar PubMed

12. Bubancova I, Kovarikova H, Laco J, Ruszova E, Dvorak O, Palicka V, et al. Next-generation sequencing approach in methylation analysis of HNF1B and GATA4 genes: searching for biomarkers in ovarian cancer. Int J Mol Sci 2017;18.10.3390/ijms18020474Search in Google Scholar PubMed PubMed Central

13. Li LC, Dahiya R. MethPrimer: designing primers for methylation PCRs. Bioinformatics (Oxford, England) 2002;18:1427–31.10.1093/bioinformatics/18.11.1427Search in Google Scholar PubMed

14. Andersen CL, Jensen JL, Orntoft TF. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res 2004;64:5245–50.10.1158/0008-5472.CAN-04-0496Search in Google Scholar PubMed

15. Moore RG, McMeekin DS, Brown AK, DiSilvestro P, Miller MC, Allard WJ, et al. A novel multiple marker bioassay utilizing HE4 and CA125 for the prediction of ovarian cancer in patients with a pelvic mass. Gynecol Oncol 2009;112:40–6.10.1016/j.ygyno.2008.08.031Search in Google Scholar PubMed PubMed Central

16. Hentze JL, Hogdall CK, Hogdall EV. Methylation and ovarian cancer: can DNA methylation be of diagnostic use? Mol Clin Oncol 2019;10:323–30.10.3892/mco.2019.1800Search in Google Scholar PubMed PubMed Central

17. Montavon C, Gloss BS, Warton K, Barton CA, Statham AL, Scurry JP, et al. Prognostic and diagnostic significance of DNA methylation patterns in high grade serous ovarian cancer. Gynecol Oncol 2012;124:582–8.10.1016/j.ygyno.2011.11.026Search in Google Scholar PubMed

18. Pisanic TR, 2nd, Cope LM, Lin SF, Yen TT, Athamanolap P, Asaka R, et al. Methylomic analysis of ovarian cancers identifies tumor-specific alterations readily detectable in early precursor lesions. Clin Cancer Res 2018;24:6536–47.10.1158/1078-0432.CCR-18-1199Search in Google Scholar PubMed PubMed Central

19. Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, et al. The GeneCards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics 2016;54:1.30.1–33.10.1002/cpbi.5Search in Google Scholar PubMed

20. Andreeva AV, Kutuzov MA. Cadherin 13 in cancer. Genes Chromosome Canc 2010;49:775–90.10.1002/gcc.20787Search in Google Scholar PubMed

21. Bol GM, Suijkerbuijk KP, Bart J, Vooijs M, van der Wall E, van Diest PJ. Methylation profiles of hereditary and sporadic ovarian cancer. Histopathology 2010;57:363–70.10.1111/j.1365-2559.2010.03642.xSearch in Google Scholar

22. Feng Q, Deftereos G, Hawes SE, Stern JE, Willner JB, Swisher EM, et al. DNA hypermethylation, Her-2/neu overexpression and p53 mutations in ovarian carcinoma. Gynecol Oncol 2008;111:320–9.10.1016/j.ygyno.2008.07.036Search in Google Scholar

23. Rathi A, Virmani AK, Schorge JO, Elias KJ, Maruyama R, Minna JD, et al. Methylation profiles of sporadic ovarian tumors and nonmalignant ovaries from high-risk women. Clin Cancer Res 2002;8:3324–31.Search in Google Scholar

24. Chmelarova M, Krepinska E, Spacek J, Laco J, Nekvindova J, Palicka V. Methylation analysis of tumour suppressor genes in ovarian cancer using MS-MLPA. Folia Biol (Praha) 2012;58:246–50.Search in Google Scholar

25. Harries LW, Perry JR, McCullagh P, Crundwell M. Alterations in LMTK2, MSMB and HNF1B gene expression are associated with the development of prostate cancer. BMC Cancer 2010;10:315.10.1186/1471-2407-10-315Search in Google Scholar

26. Setiawan VW, Haessler J, Schumacher F, Cote ML, Deelman E, Fesinmeyer MD, et al. HNF1B and endometrial cancer risk: results from the PAGE study. PLoS One 2012;7:30390.10.1371/journal.pone.0030390Search in Google Scholar

27. Tsuchiya A, Sakamoto M, Yasuda J, Chuma M, Ohta T, Ohki M, et al. Expression profiling in ovarian clear cell carcinoma: identification of hepatocyte nuclear factor-1β as a molecular marker and a possible molecular target for therapy of ovarian clear cell carcinoma. Am J Pathol 2003;163:2503–12.10.1016/S0002-9440(10)63605-XSearch in Google Scholar

28. Kao YC, Lin MC, Lin WC, Jeng YM, Mao TL. Utility of hepatocyte nuclear factor-1beta as a diagnostic marker in ovarian carcinomas with clear cells. Histopathology 2012;61:760–8.10.1111/j.1365-2559.2012.04267.xSearch in Google Scholar PubMed

29. Silva TD, Vidigal VM, Felipe AV, De Lima JM, Neto RA, Saad SS, et al. DNA methylation as an epigenetic biomarker in colorectal cancer. Oncol Lett 2013;6:1687–92.10.3892/ol.2013.1606Search in Google Scholar PubMed PubMed Central

30. Tommasi S, Karm DL, Wu X, Yen Y, Pfeifer GP. Methylation of homeobox genes is a frequent and early epigenetic event in breast cancer. Breast Cancer Res 2009;11:R14.10.1186/bcr2233Search in Google Scholar PubMed PubMed Central

31. Shen H, Fridley BL, Song H, Lawrenson K, Cunningham JM, Ramus SJ, et al. Epigenetic analysis leads to identification of HNF1B as a subtype-specific susceptibility gene for ovarian cancer. Nat Commun 2013;4:1628.10.1038/ncomms2629Search in Google Scholar PubMed PubMed Central

32. Luo ZG, Li ZG, Gui SL, Chi BJ, Ma JG. Protocadherin-17 promoter methylation in serum-derived DNA is associated with poor prognosis of bladder cancer. J Int Med Res 2014;42:35–41.10.1177/0300060513504705Search in Google Scholar PubMed

33. Yin X, Xiang T, Mu J, Mao H, Li L, Huang X, et al. Protocadherin 17 functions as a tumor suppressor suppressing Wnt/β-catenin signaling and cell metastasis and is frequently methylated in breast cancer. Oncotarget 2016;7:51720–32.10.18632/oncotarget.10102Search in Google Scholar PubMed PubMed Central

34. Akiyama Y, Watkins N, Suzuki H, Jair KW, van Engeland M, Esteller M, et al. GATA-4 and GATA-5 transcription factor genes and potential downstream antitumor target genes are epigenetically silenced in colorectal and gastric cancer. Mol Cell Biol 2003;23:8429–39.10.1128/MCB.23.23.8429-8439.2003Search in Google Scholar PubMed PubMed Central

35. Guo M, Akiyama Y, House MG, Hooker CM, Heath E, Gabrielson E, et al. Hypermethylation of the GATA genes in lung cancer. Clin Cancer Res 2004;10:7917–24.10.1158/1078-0432.CCR-04-1140Search in Google Scholar PubMed

36. Wakana K, Akiyama Y, Aso T, Yuasa Y. Involvement of GATA-4/-5 transcription factors in ovarian carcinogenesis. Cancer Letters 2006;241:281–8.10.1016/j.canlet.2005.10.039Search in Google Scholar PubMed

37. Chmelarova M, Dvorakova E, Spacek J, Laco J, Palicka V. Importance of promoter methylation of GATA4 gene in epithelial ovarian cancer. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 2013;157:294–7.10.5507/bp.2013.079Search in Google Scholar PubMed

Received: 2019-12-23
Accepted: 2020-02-04
Published Online: 2020-03-07
Published in Print: 2020-07-28

©2020 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/cclm-2019-1319
Keywords for this article
biomarker; DNA methylation; high-grade serous ovarian cancer

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Quality controls for serology: an unfinished agenda
  4. A modern and pragmatic definition of Laboratory Medicine
  5. Reviews
  6. Blood biochemical characteristics of patients with coronavirus disease 2019 (COVID-19): a systemic review and meta-analysis
  7. ISO/TS 20914:2019 – a critical commentary
  8. Mini Review
  9. Reporting of D-dimer data in COVID-19: some confusion and potential for misinformation
  10. Opinion Paper
  11. Implementation of metrological traceability in laboratory medicine: where we are and what is missing
  12. IFCC Recommendation
  13. Recommendation for performance verification of patient-based real-time quality control
  14. Genetics and Molecular Diagnostics
  15. Comparison of BCR-ABL1 quantification in peripheral blood and bone marrow using an International Scale-standardized assay for assessment of deep molecular response in chronic myeloid leukemia
  16. General Clinical Chemistry and Laboratory Medicine
  17. Risk assessment of the total testing process based on quality indicators with the Sigma metrics
  18. Determination of hemolysis cut-offs for biochemical and immunochemical analytes according to their value
  19. A computer model for professional competence assessment according to ISO 15189
  20. Traceability validation of six enzyme measurements on the Abbott Alinity c analytical system
  21. Evaluating the need for free glycerol blanking for serum triglyceride measurements at Charlotte Maxeke Johannesburg Academic Hospital
  22. Challenges of LC-MS/MS ethyl glucuronide analysis in abstinence monitoring of liver transplant candidates
  23. Changes in the result of antinuclear antibody immunofluorescence assay on HEp-2 cells reflect disease activity status in systemic lupus erythematosus
  24. Reference Values and Biological Variations
  25. Long-term biological variation estimates of 13 hematological parameters in healthy Chinese subjects
  26. Age-specific reference values improve the diagnostic performance of AMH in polycystic ovary syndrome
  27. Establishment of reference intervals for immunoassay analytes of adult population in Saudi Arabia
  28. Hematology and Coagulation
  29. Total haemoglobin – a reference measuring system for improvement of standardisation
  30. Laboratory testing for activated protein C resistance: rivaroxaban induced interference and a comparative evaluation of andexanet alfa and DOAC Stop to neutralise interference
  31. Cancer Diagnostics
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  34. Diabetes
  35. Availability and analytical quality of hemoglobin A1c point-of-care testing in general practitioners’ offices are associated with better glycemic control in type 2 diabetes
  36. Infectious Diseases
  37. Validation of a chemiluminescent assay for specific SARS-CoV-2 antibody
  38. Dynamic profile and clinical implications of hematological parameters in hospitalized patients with coronavirus disease 2019
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  44. Counting and reporting band count is unreliable practice due to the high inter-observer variability
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  46. How reliable is the detection of anti-mitochondrial antibodies on murine triple-tissue?
  47. Further advices on measuring lipoprotein(a) for reducing the residual cardiovascular risk on statin therapy
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