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Original Research

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The importance of dysregulated miRNAs on ovarian cysts and epithelial ovarian cancer

  • Ece Gumusoglu1
  • Tuba Gunel1,*,
  • Mohammad Kazem Hosseini1
  • Nogayhan Seymen2
  • Taylan Senol3
  • Uğur Sezerman2
  • Samet Topuz4
  • Kılıç Aydınlı5

1Istanbul University, Faculty of Science, Department of Molecular Biology and Genetics, 34134, Istanbul, Turkey

2Acıbadem University, School of Medicine, Department of Basic Sciences, Biostatistics, 34684, Istanbul Turkey

3Istanbul Zeynep Kamil Women and Child Diseases Training and Research Hospital, 34668, Istanbul, Turkey

4Istanbul University, Istanbul Medical Faculty, Department of Obstetrics and Gynecology, 34093, Istanbul, Turkey

5Medicus Healthcare Centre, 34365, Istanbul, Turkey

DOI: 10.31083/j.ejgo.2021.01.2167 Vol.42,Issue 1,February 2021 pp.66-72

Submitted: 09 June 2020 Accepted: 16 October 2020

Published: 15 February 2021

*Corresponding Author(s): Tuba Gunel E-mail: gunel@istanbul.edu.tr

Abstract

Objective: Benign ovarian cysts (BOC) are the most common tumors in women of reproductive age. Usually, these cysts are harmless, but, a small number of them occasionally progress to malignancy. Among ovarian malignancies, epithelial ovarian cancer (EOC) comprises 90% and is the most important cause of gynecologic cancer-related deaths. We aimed to identify dysregulated miRNAs in patients with benign ovarian cysts (n = 11) compared to EOC (n = 10) and to healthy individuals (HI) (n = 15). Methods: The serum samples from EOC and BOC patients were collected before operation. We studied three different sample groups (serum of EOC (n = 8), HI (n = 8), and BOC (n = 8) patients) that contained the highest-quality of RNA. Microarray data were analyzed according to expression of miRNAs and target genes by bioinformatics tools. Results: When compared to EOC samples, 75 miRNAs were dysregulated in BOC samples. Sixty-six miRNAs from BOC were dysregulated when compared to HI samples. Bioinformatics analysis of BOC vs. EOC and BOC vs. HI showed that 46 miRNAs were congruent and their expression alterations were similar (up- or down-regulated). Further analysis showed that these 46 miRNAs are associated to one of three pathways involved in cancer pathogenesis. Conclusion: Several miRNAs might play a role in BOC formation and/or malignant transformation. These dysregulated miRNAs could potentially be a biomarker to distinguish between a completely BOC and one that is malignant or has potential for malignant transformation.


Keywords

Ovarian neoplasms; Ovarian cysts; MicroRNAs; Neoplastic transformation; Ep-ithelial ovarian cancer


Cite and Share

Ece Gumusoglu,Tuba Gunel,Mohammad Kazem Hosseini,Nogayhan Seymen,Taylan Senol,Uğur Sezerman,Samet Topuz,Kılıç Aydınlı. The importance of dysregulated miRNAs on ovarian cysts and epithelial ovarian cancer. European Journal of Gynaecological Oncology. 2021. 42(1);66-72.

References

[1] Onur MR, Bakal U, Kocakoc E, Tartar T, Kazez A. Cystic abdominal masses in children: a pictorial essay. Clinical and Translational Imaging. 2013; 37: 18-27.

[2] Wang Y, Sundfeldt K, Mateoiu C, Shih Ie M, Kurman RJ, Schaefer J, et al. Diagnostic potential of tumor DNA from ovarian cyst fluid. Elife. 2016; e15175.

[3] Lee HJ, Woo SK, Kim JS, Suh SJ. “Daughter cyst” sign: a sonographic finding of ovarian cyst in neonates, infants, and young children. American Journal of Roentgenology. 2000; 174: 1013-1015.

[4] Woo YM, Park JH. MicroRNA biomarkers in cystic diseases. BMB Reports. 2013; 46: 338-345.

[5] Patel S. Polycystic ovary syndrome (PCOS), an inflammatory, systemic, lifestyle endocrinopathy. The Journal of Steroid Biochemistry and Molecular Biology. 2018; 182: 27-36.

[6] Prefumo F, Rossi AC. Endometriosis, endometrioma, and ART results: current understanding and recommended practices. Best Practice & Research Clinical Obstetrics & Gynaecology. 2018; 51: 34-40.

[7] Cheng EJ, Kurman RJ, Wang M, Oldt R, Wang BG, Berman DM, et al. Molecular genetic analysis of ovarian serous cystadenomas. Laboratory Investigation. 2004; 84: 778-784.

[8] Levine D, Brown DL, Andreotti RF, Benacerraf B, Benson CB, Brewster WR, et al. Management of asymptomatic ovarian and other adnexal cysts imaged at US: Society of Radiologists in Ultrasound Consensus Conference Statement. Radiology. 2010; 256: 943- 954.

[9] Chu CS, Rubin SC. Screening for ovarian cancer in the general population. Best Practice & Research Clinical Obstetrics & Gynaecology. 2006; 20: 307-320.

[10] Gumusoglu E, Gunel T. The role of circulating biomarkers in the early diagnosis of ovarian cancer. 2018.

[11] Dubeau L. The cell of origin of ovarian epithelial tumours. The Lancet Oncology. 2008; 9: 1191-1197.

[12] Rodriguez M, Dubeau L. Ovarian tumor development: insights from ovarian embryogenesis. European Journal of Gynaecological Oncology. 2001; 22: 175-183.

[13] Serov SF, Scully RE, Sobin LH, World Health O. Histological typing of ovarian tumours / S. F. Serov, R. E. Scully, in collaboration with L. H. Sobin and pathologists in ten countries. World Health Organization. 1973.

[14] Thor AD, Young RH, Clement PB. Pathology of the fallopiantube, broad ligament, peritoneum, and pelvic soft-tissues. Human Pathology. 1991; 22: 856-867.

[15] Yang J, Zhou Y, Ng SK, Huang KC, Ni X, Choi PW, et al. Characterization of MicroRNA-200 pathway in ovarian cancer and serous intraepithelial carcinoma of fallopian tube. BMC Cancer. 2017; 17: 422.

[16] Retamales-Ortega R, Orostica L, Vera C, Cuevas P, Hernandez A, Hurtado I, et al. Role of Nerve Growth Factor (NGF) and miRNAs in epithelial ovarian cancer. International Journal of Molecular Sciences. 2017; 18: 507.

[17] Zhang B, Cai FF, Zhong XY. An overview of biomarkers for the ovarian cancer diagnosis. European Journal of Obstetrics & Gynecology and Reproductive Biology. 2011; 158: 119-123.

[18] Alles J, Fehlmann T, Fischer U, Backes C, Galata V, Minet M, et al. An estimate of the total number of true human miRNAs. Nucleic Acids Research. 2019; 47: 3353-3364.

[19] Gold B, Cankovic M, Furtado LV, Meier F, Gocke CD. Do circulating tumor cells, exosomes, and circulating tumor nucleic acids have clinical utility? A report of the association for molecular pathology. Journal of Molecular Diagnostics. 2015; 17: 209-224.

[20] Chen SN, Chang R, Lin LT, Chern CU, Tsai HW, Wen ZH, et al. MicroRNA in ovarian cancer: biology, pathogenesis, and therapeutic opportunities. International Journal of Environmental Research and Public Health. 2019; 16: 1510.

[21] Gunel T, Dogan B, Gumusoglu E, Hosseini M, Topuz S, Aydinli K. Regulation of HMGA2 and KRAS genes in epithelial ovarian cancer by miRNA hsa-let-7d-3p. Journal of Cancer Research and Therapeutics. 2019; 15: 1321-1327.

[22] Günel T, Gumusoglu E, Dogan B, Ertem FB, Hosseini MK, Cevik N, et al. Potential biomarker of circulating hsa-miR-1273g-3p level for detection of recurrent epithelial ovarian cancer. Archives of Gynecology and Obstetrics. 2018; 298: 1173-1180.

[23] Yokoi A, Matsuzaki J, Yamamoto Y, Yoneoka Y, Takahashi K, Shimizu H, et al. Integrated extracellular microRNA profiling for ovarian cancer screening. Nature Communications. 2018; 9: 4319.

[24] R core team. R: a language and environment for statistical computing, r foundation for statistical computing. Vienne, Austria. 2019.

[25] Champely S. Pwr: basic functions for power analysis. 2020.

[26] Kok MGM, de Ronde MWJ, Moerland PD, Ruijter JM, Creemers EE, Pinto-Sietsma SJ. Small sample sizes in high-throughput miRNA screens: a common pitfall for the identification of miRNA biomarkers. Biomolecular Detection and Quantification. 2018; 15: 1- 5.

[27] Cohen J. Statistical power analysis. Current Directions in Psychological Science. 1992; 1: 98-101.

[28] Ritchie ME, Phipson B, Wu D, Hu YF, Law CW, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research. 2015; 43: e47.

[29] Cavalcanti E, Galleggiante V, Coletta S, Stasi E, Chieppa M, Armentano R, et al. Altered miRNAs expression correlates with gastroenteropancreatic neuroendocrine tumors grades. Frontiers in Oncology. 2020; 10: 1187.

[30] Vlachos IS, Zagganas K, Paraskevopoulou MD, Georgakilas G, Karagkouni D, Vergoulis T, et al. DIANA-miRPath v3.0: deci-phering microRNA function with experimental support. Nucleic Acids Research. 2015; 43: W460-W466.

[31] Eneh S, Heikkinen S, Hartikainen JM, Kuopio T, Mecklin JP, Kosma VM, et al. MicroRNAs associated with biological pathways of left- and right-sided colorectal cancer. Anticancer Research. 2020; 40: 3713-3722.

[32] Tang S, Wang X, Deng T, Ge H, Xiao X. Identification of C3 as a therapeutic target for diabetic nephropathy by bioinformatics analysis. Scientific Reports. 2020; 10: 13468.

[33] Kanehisa M. Toward understanding the origin and evolution of cellular organisms. Protein Science. 2019; 28: 1947-1951.

[34] Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Research. 2000; 28: 27-30.

[35] Kanehisa M, Sato Y, Furumichi M, Morishima K, Tanabe M. New approach for understanding genome variations in KEGG. Nucleic Acids Research. 2019; 47: D590-D595.

[36] Alam S, Anugraham M, Huang YL, Kohler RS, Hettich T, Winkelbach K, et al. Altered (neo-) lacto series glycolipid biosynthesis impairs α2-6 sialylation on N-glycoproteins in ovarian cancer cells. Scientific Reports. 2017; 7: 45367.

[37] Blanas A, Sahasrabudhe NM, Rodríguez E, van Kooyk Y, van Vliet SJ. Fucosylated antigens in cancer: an alliance toward tumor progression, metastasis, and resistance to chemotherapy. Frontiers in Oncology. 2018; 8: 39.

[38] KEGG. Kegg: kyoto encyclopedia of genes and genomes. Japan: Kanehisa Laboratories. 2019.

[39] Walker C, Mojares E, Hernandez AD. Role of extracellular matrix in development and cancer progression. International Journal of Molecular Sciences. 2018; 19: 3028.

[40] Srivastava SK, Ahmad A, Zubair H, Miree O, Singh S, Rocconi RP, et al. MicroRNAs in gynecological cancers: small molecules with big implications. Cancer Letters. 2017; 407: 123-138.

[41] Crum CP, Drapkin R, Miron A, Ince TA, Muto M, Kindelberger DW, et al. The distal fallopian tube: a new model for pelvic serous carcinogenesis. Current Opinion in Obstetrics & Gynecology. 2007; 19: 3-9.

[42] Eckert MA, Pan S, Hernandez KM, Loth RM, Andrade J, Volchenboum SL, et al. Genomics of ovarian cancer progression reveals diverse metastatic trajectories including intraepithelial metastasis to the fallopian tube. Cancer Discovery. 2016; 6: 1342-1351.

[43] Kurman RJ, Shih IM. The origin and pathogenesis of epithelial ovarian cancer: a proposed unifying theory. American Journal of Surgical Pathology. 2010; 34: 433-443.

[44] Medeiros F, Muto MG, Lee Y, Elvin JA, Callahan MJ, Feltmate C, et al. The tubal fimbria is a preferred site for early adenocarcinoma in women with familial ovarian cancer syndrome. American Journal of Surgical Pathology. 2006; 30: 230-236.

[45] Singh N, Gilks CB, Hirschowitz L, Kehoe S, McNeish IA, Miller D, et al. Primary site assignment in tubo-ovarian high-grade serous carcinoma: consensus statement on unifying practice worldwide. Gynecologic Oncology. 2016; 141: 195-198.

[46] Zong XY, Nephew KP. Ovarian cancer stem cells: role in metas-tasis and opportunity for therapeutic targeting. Cancers. 2019; 11: 934.

[47] Disaia P CW. Germ cell stromal and other ovarian tumours. Clinical Gynaecological Oncology. 1997; 351-371.

[48] Sagae S, Kudo R. Surgery for germ cell tumors. Annals of Surgical Oncology. 2000; 19: 76-81.

[49] Curling OM, Potsides PN, Hudson CN. Malignant change in benign cystic teratoma of the ovary. British Journal of Obstetrics and Gynaecology. 1979; 86: 399-402.

[50] Caspi B, Lerner-Geva L, Dahan M, Chetrit A, Modan B, Hagay Z, et al. A possible genetic factor in the pathogenesis of ovarian dermoid cysts. Gynecologic and Obstetric Investigation. 2003; 56: 203- 206.

[51] Park CH, Jung MH, Ji YI. Risk factors for malignant transformation of mature cystic teratoma. Obstetrics and Gynecology Science. 2015; 58: 475-480.

[52] Tavassoli FA DP. Pathology and genetics: tumours of the breast and female genital organs. classification of tumours working group on gynecologic tumours. tumors of the ovary and peritoneum. IARC Press. 2003; 124-127.

[53] Wan WN, Zhang YQ, Wang XM, Liu YJ, Zhang YX, Que YH, et al. Down-regulated miR-22 as predictive biomarkers for prognosis of epithelial ovarian cancer. Diagnostic Pathology. 2014; 9: 178.

[54] Cha SY, Choi YH, Hwang S, Jeong JY, An HJ. Clinical impact of micrornas associated with cancer stem cells as a prognostic factor in ovarian carcinoma. Journal of Cancer. 2017; 8: 3538-3547.

[55] Chong GO, Jeon HS, Han HS, Son JW, Lee YH, Hong DG, et al. Differential MicroRNA expression profiles in primary and recurrent epithelial ovarian cancer. Anticancer Research. 2015; 35: 2611-2617.

[56] Li Y, Yao L, Liu F, Hong J, Chen L, Zhang B, et al. Characterization of microRNA expression in serous ovarian carcinoma. International Journal of Molecular Medicine. 2014; 34: 491-498.

[57] Pandey R, Woo HH, Varghese F, Zhou M, Chambers SK. Circulating miRNA profiling of women at high risk for ovarian cancer. Translational Oncology. 2019; 12: 714-725.

[58] Reza AM, Choi YJ, Yasuda H, Kim JH. Human adipose mesenchymal stem cell-derived exosomal-miRNAs are critical factors for inducing anti-proliferation signalling to A2780 and SKOV-3 ovarian cancer cells. Scientific Reports. 2016; 6: 38498.

[59] Zhuo DH, Li X, Guan F. Biological roles of aberrantly expressed glycosphingolipids and related enzymes in human cancer development and progression. Frontiers in Physiology. 2018; 9: 466.

[60] Jacob F, Anugraham M, Pochechueva T, Tse BWC, Alam S, Guertler R, et al. The glycosphingolipid P-1 is an ovarian cancer-associated carbohydrate antigen involved in migration. British Journal of Cancer. 2014; 111: 1634-1645.

[61] Zhang T, de Waard AA, Wuhrer M, Spaapen RM. The role of glycosphingolipids in immune cell functions. Frontiers in Immunology. 2019; 10: 90.

[62] Rajanayake KK, Taylor WR, Isailovic D. The comparison of glycosphingolipids isolated from an epithelial ovarian cancer cell line and a nontumorigenic epithelial ovarian cell line using MALDI-MS and MALDI-MS/MS. Carbohydrate Research. 2016; 431: 6-14.

[63] Tran DT, Ten Hagen KG. Mucin-type O-Glycosylation during development. Journal of Biological Chemistry. 2013; 288: 6921-6929.

[64] Chugh S, Gnanapragassam VS, Jain M, Rachagani S, Ponnusamy MP, Batra SK. Pathobiological implications of mucin glycans in cancer: Sweet poison and novel targets. Biochimica et Biophysica Acta. 2015; 1856: 211-225.

[65] Akita K, Yoshida S, Ikehara Y, Shirakawa S, Toda M, Inoue M, et al. Different levels of sialyl-Tn antigen expressed on MUC16 in patients with endometriosis and ovarian cancer. International Journal of Gynecological Cancer. 2012; 22: 531-538.

[66] Brockhausen I. Mucin-type O-glycans in human colon and breast cancer: glycodynamics and functions. EMBO Reports. 2006; 7: 599- 604.

[67] Eble JA, Niland S. The extracellular matrix in tumor progression and metastasis. Clinical & Experimental Metastasis. 2019; 36: 171-198.

[68] Cho A, Howell VM, Colvin EK. The extracellular matrix in epithelial ovarian cancer-a piece of a puzzle. Frontiers in Oncology. 2015; 5: 245.

[69] Januchowski R, Zawierucha P, Ruciński M, Zabel M. Microarray-based detection and expression analysis of extracellular matrix proteins in drug‑resistant ovarian cancer cell lines. Oncology Reports. 2014; 32: 1981-1990.


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