Serum Biomarker Based Algorithms in Diagnosis of Ovarian Cancer: A Review
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Epithelial ovarian cancer accounts for more than 90% of ovarian tumours and continues as a leading cause of death from gynaecological malignancies. It is often difficult to differentiate a benign ovarian mass from malignant ones. Invasive histopathological biopsy is used as the gold standard diagnostic tool to diagnose cancer in patients with ovarian mass. A wide spectrum of Biomarkers were tried in various studies to develop a non invasive diagnostic tool, out of which HE4 and CA 125 remain the only clinically useful biomarker. Consequently various Biomarker based algorithms i.e. Risk of Malignancy Index, risk of ovarian cancer algorithm, OVA1, risk of malignancy algorithm were generated that have been developed to assess the risk of a mass being malignant. These algorithms help in timely triage of patients. Recently in 2016 FDA cleared Ova1 test (OVERA) with CA 125-II, HE4, apolipoprotein A-1, FSH, and transferring (Sensitivity 91% and Specificity 69%) as a referral or Triage test in patients presenting with ovarian mass. Combination of protein and circulating Micro RNA analysis in blood, could provide a comprehensive screening and diagnostic panel, in management of patients presenting with ovarian mass in one clinical setting.
KeywordsEpithelial ovarian cancer Biomarkers Biomarker based algorithms Circulating Micro RNA
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Author declares that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by the authors.
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For this type of study formal consent is not required. This article does not contain any studies with human participants or animals performed by the authors.
- 2.Martina M, Elisa D, Orazio R, Valentina B, Teresita N, Matte G, et al. The ROMA (Risk of ovarian Malignancy algorithm) for estimating the risk of epithelial ovarian cancer in women presenting with pelvic mass: is it really useful? Clin Chem Lab Med. 2011;49(3):521–5.Google Scholar
- 4.Robert CBJ, Steven S, Anna L, Richard GM. Differential diagnosis of a pelvic mass: improved algorithms and novel biomarkers. Int J Gynecol Cancer. 2012;22(1):S5–8.Google Scholar
- 7.Kim YM, Whang DH, Park J, Kim SH, Lee SW, Park HA, et al. Evaluation of the accuracy of human epididymis protein 4(HE4) in combination with CA 125 for detecting ovarian cancer-a prospective case control study in a Korean population. Clin Chem Med. 2011;49(3):527–34.Google Scholar
- 8.Bandiera E, Romani C, Specchia C, Zanoti L, Galli C, Ruggeri G, et al. Serum human epididymis protein 4(HE4) and risk of ovarian malignancy algorithm (ROMA) as new diagnostic and prognostic tool for epithelial ovarian cancer management. Cancer Epidemiol Biomark Prev. 2011;20(12):2496–506.CrossRefGoogle Scholar
- 9.Su F, Kozak KR, Imaizumi S, Satoshi I, Gaoa F, Malaika WA, et al. Apolipoprotein A-I (apoA-I) and apoA-I mimetic peptides inhibit tumor development in a mouse model of ovarian cancer. Proc Nat Acad Sci USA. 2010;107(46):19997–20002. https://doi.org/10.1073/pnas.1009010107.CrossRefPubMedGoogle Scholar
- 16.Moore RG, Miller MC, Disilvestro P, Landrum LM, Gajewski W, Ball JJ, et al. Evaluation of the diagnostic accuracy of the risk of ovarian malignancy algorithm in women with a pelvic mass. Obstet Gynecol. 2011;118(2 Pt 1):280–8. https://doi.org/10.1097/AOG.0b013e318224fce2.CrossRefPubMedPubMedCentralGoogle Scholar
- 23.Hentze JL, Hogdall C, Kjaer SK, Blaakaer J, Hogdall E. Searching for new biomarkers in ovarian cancer patients: rationale and design of a retrospective study under the Mermaid III project. Contemp Clin Trials Commun. 2017;13(8):167–74. https://doi.org/10.1016/j.conctc.2017.10.003 Ecollection 2017 Dec.CrossRefGoogle Scholar
- 29.Pendlebury A, Hannan NJ, Binder N, Beard S, Mcgauran M, Grant P, et al. The circulating microRNA-200 family in whole blood are potential biomarkers for high-grade serous epithelial ovarian cancer. Biomed Rep. 2017;6(3):319–22. https://doi.org/10.3892/br.2017.847 Epub 2017 Jan 25.CrossRefPubMedPubMedCentralGoogle Scholar
- 30.Kafshdooz L, Pourfathi H, Akbarzadeh A, Kafshdooz T, Razban Z, Sheervalilou R, et al. The role of microRNAs and nanoparticles in ovarian cancer: a review. Artif Cells Nanomed Biotechnol. 2018;23:1–7. https://doi.org/10.1080/21691401.2018.1454931 [Epub ahead of print] PMID: 29569937.CrossRefGoogle Scholar