Research Article

Identification of key factors associated with early- and late-onset ovarian serous cystadenocarcinoma

Shuang Ma

Yang Zheng

Chengwei Fei

Shuang Ma

*Author for correspondence:

E-mail Address: mashuang717@126.com

https://orcid.org/0000-0001-8776-7375

Ministry of Education Key Laboratory of Cell Proliferation & Regulation Biology, College of Life Sciences, Beijing Normal University, Beijing, 100875, PR China

Search for more papers by this author

Yang Zheng

Genenexus Technology Corporation, Shanghai, 200438, PR China

Search for more papers by this author

Chengwei Fei

Department of Aeronautics & Astronautics, Fudan University, Shanghai, 200433, PR China

Search for more papers by this author

Published Online:4 Sep 2020https://doi.org/10.2217/fon-2020-0668

View article

Abstract

Aim: To uncover the molecular mechanisms of early-onset ovarian serous cystadenocarcinoma (EOOSC; patients <50 years old) and late-onset ovarian serous cystadenocarcinoma (LOOSC; patients ≥50 years old). Materials & methods: Bioinformatics was utilized to identify the key factors. Results: 478 EOOSC and 899 LOOSC individual differentially expressed genes were identified and enriched in different pathways. The expression of key genes LAG3, LRRC63 and MT1B significantly influenced the overall survival of EOOSC patients. The expression of key genes RDH12, NTSR1, ZSCAN16, CT45A3 and EPPIN_WFDC6 significantly affected the overall survival of LOOSC patients. Conclusions: The molecular mechanisms of EOOSC and LOOSC appear to be different, so that patients might be treated individually in respect of age.

Lay abstract

The chances of surviving ovarian cancer decrease as you get older. Ovarian serous cystadenocarcinoma is the most common and deadly type of ovarian cancer. Research has shown that there are differences between younger and older patients in relation to how the cancerous ovarian serous cystadenocarcinoma tumor behaves and how the body responds to the cancer. Our research is the first to show which genes could be responsible for these differences and which genes become more or less active as you grow older. Our results suggest that ovarian serous cystadenocarcinoma patients might need to be treated differently with respect to their ages.

Keywords:

Papers of special note have been highlighted as: • of interest; •• of considerable interest

References

1. Ferlay J, Soerjomataram I, Dikshit R et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136, e359–e386 (2015).
Medline CASGoogle Scholar
2. Gaba F, Blyuss O, Liu X et al. Population study of ovarian cancer risk prediction for targeted screening and prevention. Cancers (Basel) 12, E1241 (2020).
Medline CASGoogle Scholar
3. Staples JN, Peres LC, Camacho F et al. Cardiometabolic comorbidities and epithelial ovarian cancer risk among African–American women in the African–American Cancer Epidemiology Study (AACES). Gynecol. Oncol. 158(1), 123–129 (.2020).
Medline CASGoogle Scholar
4. Davidson B. Biomarkers of drug resistance in ovarian cancer – an update. Expert. Rev. Mol. Diagn. 19, 469–476 (2019).
Medline CASGoogle Scholar
5. Stewart C, Ralyea C, Lockwood S. Ovarian cancer: an integrated review. Semin. Oncol. Nurs. 35, 151–156 (2019).
MedlineGoogle Scholar
6. Gupta KK, Gupta VK, Naumann RW. Ovarian cancer: screening and future directions. Int. J. Gynecol. Cancer 29, 195–200 (2019).
MedlineGoogle Scholar
7. Reid BM, Permuth JB, Sellers TA. Epidemiology of ovarian cancer: a review. Cancer Biol. Med. 14, 9–32 (2017).
Medline CASGoogle Scholar
8. Matulonis UA, Sood AK, Fallowfield L et al. Ovarian cancer. Nat. Rev. Dis. Primers 2, 16061 (2016).
MedlineGoogle Scholar
9. Bozas G, Dimopoulos MA, Kastritis E et al. Young age is associated with favorable characteristics but is not an independent prognostic factor in patients with epithelial ovarian cancer: a single institution experience. Oncology 70, 265–272 (2006). •• Suggests that young patients (≤40 years old) with epithelial ovarian cancer have low tumor grade, more complete surgical debulking and better performance status compared with older patients.
MedlineGoogle Scholar
10. Yoshikawa N, Kajiyama H, Mizuno M et al. Clinicopathologic features of epithelial ovarian carcinoma in younger vs older patients: analysis in Japanese women. J. Gynecol. Oncol. 25, 118–123 (2014). •• Indicates that younger and older patients with epithelial ovarian cancer have different clinical features, particularly regarding the stage of the disease and pathological distribution.
MedlineGoogle Scholar
11. Cheng HY, Zeng L, Ye X et al. Age and menopausal status are important factors influencing the serum human epididymis secretory protein 4 level: a prospective cross-sectional study in healthy Chinese people. Chin. Med. J. (Engl.) 133(11), 1285–1291 (.2020). • Reports that age and menopausal status influence the level of serum human epididymis secretory protein 4 which is a new biomarker of ovarian cancer.
MedlineGoogle Scholar
12. Elzakkers JCJ, van der Aa MA, van Altena AM et al. Further insights into the role of tumour characteristics in survival of young women with epithelial ovarian cancer. Gynecol. Oncol. 155, 213–219 (2019).
Medline CASGoogle Scholar
13. Chan JK, Loizzi V, Lin YG et al. Stages III and IV invasive epithelial ovarian carcinoma in younger versus older women: what prognostic factors are important? Obstet. Gynecol. 102, 156–161 (2003).
MedlineGoogle Scholar
14. Harris MA, Deegan JI, Lomax J et al. The gene ontology project in 2008. Nucleic Acids Res. 36, 440–444 (2008).
Google Scholar
15. Consortium GO. The Gene Ontology (GO) project in 2006. Nucleic Acids Res. 34, 322–326 (2006).
MedlineGoogle Scholar
16. Consortium TGO Ashburner M, Ball CA et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).
MedlineGoogle Scholar
17. Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 27, 29–34 (2000).
Google Scholar
18. Armer JS, Clevenger L, Davis LZ et al. Life stress as a risk factor for sustained anxiety and cortisol dysregulation during the first year of survivorship in ovarian cancer. Cancer 124, 3401–3408 (2018).
Medline CASGoogle Scholar
19. Cuneo MG, Schrepf A, Slavich GM et al. Diurnal cortisol rhythms, fatigue and psychosocial factors in five-year survivors of ovarian cancer. Psychoneuroendocrinology 84, 139–142 (2017).
Medline CASGoogle Scholar
20. Schrepf A, Thaker PH, Goodheart MJ et al. Diurnal cortisol and survival in epithelial ovarian cancer. Psychoneuroendocrinology 53, 256–267 (2015).
Medline CASGoogle Scholar
21. Kulkarni JN, Mistry RC, Kamat MR, Chinoy R, Lotlikar RG. Autonomous aldosterone-secreting ovarian tumor. Gynecol. Oncol. 37, 284–289 (1990).
Medline CASGoogle Scholar
22. Todesco S, Terribile V, Borsatti A, Mantero F. Primary aldosteronism due to a malignant ovarian tumor. J. Clin. Endocrinol. Metab. 41, 809–819 (1975).
Medline CASGoogle Scholar
23. Pollak M. Insulin and insulin-like growth factor signalling in neoplasia. Nat. Rev. Cancer 8, 915–928 (2008).
Medline CASGoogle Scholar
24. Liefers-Visser JAL, Meijering RAM, Reyners AKL, van der Zee AGJ, de Jong S. IGF system targeted therapy: therapeutic opportunities for ovarian cancer. Cancer Treat. Rev. 60, 90–99 (2017).
Medline CASGoogle Scholar
25. Shibata K, Kajiyama H, Mizokami Y et al. Placental leucine aminopeptidase (P-LAP) and glucose transporter 4 (GLUT4) expression in benign, borderline, and malignant ovarian epithelia. Gynecol. Oncol. 98, 11–18 (2005).
Medline CASGoogle Scholar
26. He Y, Gu Z, Zhu Q et al. CA125 over-release behavior following a 75-g oral glucose test as a predictive biomarker of multidrug resistance in patients with ovarian cancer. Int. J. Cancer 145, 1690–1700 (2019). •• Verified that 75-gram oral glucose and insulin peaks can synergistically increase ovarian cancer-derived CA125 levels, which is a prognostic factor of poor outcome.
Medline CASGoogle Scholar
27. Li Y, Fang Y, Liu Y, Yang X. MicroRNAs in ovarian function and disorders. J. Ovarian Res. 8, 51 (2015).
MedlineGoogle Scholar
28. Trabert B, Michels KA, Anderson GL et al. Circulating androgens and postmenopausal ovarian cancer risk in the Women's Health Initiative Observational Study. Int. J. Cancer 145, 2051–2060 (2019).
Medline CASGoogle Scholar
29. Nieman KM, Kenny HA, Penicka CV et al. Adipocytes promote ovarian cancer metastasis and provide energy for rapid tumor growth. Nat. Med. 17, 1498–1503 (2011).
Medline CASGoogle Scholar
30. Beaufort CM, Helmijr JC, Piskorz AM et al. Ovarian cancer cell line panel (OCCP): clinical importance of in vitro morphological subtypes. PLoS One 9, e103988 (2014).
MedlineGoogle Scholar
31. Wang J, Wu A, Yang B, Zhu X, Teng Y, Ai Z. Profiling and bioinformatics analyses reveal differential circular RNA expression in ovarian cancer. Gene 724, 144150 (2020).
Medline CASGoogle Scholar
32. Zhang W, Peng P, Ou X, Shen K, Wu X. Ovarian cancer circulating extracelluar vesicles promote coagulation and have a potential in diagnosis: an iTRAQ based proteomic analysis. BMC Cancer 19, 1095 (2019).
MedlineGoogle Scholar
33. Xu Y, Wang W, Chen J et al. High neuropilin and tolloid-like 1 expression associated with metastasis and poor survival in epithelial ovarian cancer via regulation of actin cytoskeleton. J. Cell. Mol. Med. 24(16), 9114–9124 (.2020). •• Revealed that regulation of actin cytoskeleton pathway played an important role in metastasis and poor survival in epithelial ovarian cancer.
Medline CASGoogle Scholar
34. Del Porto F, Cifani N, Proietta M et al. Lag3+ regulatory T lymphocytes in critical carotid artery stenosis. Clin. Exp. Med. 19, 463–468 (2019).
MedlineGoogle Scholar
35. Zhang J, Liu W, Xie T et al. Elevated LAG-3 on CD4+ T cells negatively correlates with neutralizing antibody response during HCV infection. Immunol. Lett. 212, 46–52 (2019).
Medline CASGoogle Scholar
36. Feng X, Tang R, Zhang R et al. A comprehensive analysis of IDO1 expression with tumour-infiltrating immune cells and mutation burden in gynaecologic and breast cancers. J. Cell. Mol. Med. 24(9), 5238–5248 (2020).
Medline CASGoogle Scholar
37. Tu L, Guan R, Yang H et al. Assessment of the expression of the immune checkpoint molecules PD-1, CTLA4, TIM-3 and LAG-3 across different cancers in relation to treatment response, tumor-infiltrating immune cells and survival. Int. J. Cancer 147, 423–439 (2020).
Medline CASGoogle Scholar
38. Norris EJ, Zhang Q, Jones WD et al. Increased expression of neurotensin in high grade serous ovarian carcinoma with evidence of serous tubal intraepithelial carcinoma. J. Pathol. 248, 352–362 (2019).
Medline CASGoogle Scholar
39. Liu J, Agopiantz M, Poupon J et al. Neurotensin receptor 1 antagonist SR48692 improves response to carboplatin by enhancing apoptosis and inhibiting drug efflux in ovarian cancer. Clin. Cancer Res. 23, 6516–6528 (2017).
Medline CASGoogle Scholar
40. Corona RI, Seo JH, Lin X et al. Non-coding somatic mutations converge on the PAX8 pathway in ovarian cancer. Nat. Commun. 11, 2020 (2020).
Medline CASGoogle Scholar
41. Dou YD, Huang T, Wang Q et al. Integrated microRNA and mRNA signatures in peripheral blood lymphocytes of familial epithelial ovarian cancer. Biochem. Biophys. Res. Commun. 496, 191–198 (2018).
Medline CASGoogle Scholar
42. Gu CL, Zhang Z, Fan WS et al. Identification of microRNAs as potential biomarkers in ovarian endometriosis. Reprod. Sci. 27(9), 1715–1723 (.2020).
Medline CASGoogle Scholar
43. Shen W, Xie X, Liu M, Wang L. Diagnostic value of lncRNA ROR in differentiating ovarian cancer patients. Clin. Lab. 66, doi:10.7754/Clin.Lab.2019.191035 (.2020).
Google Scholar
44. Zhou J, Dong ZN, Qiu BQ et al. CircRNA FGFR3 induces epithelial–mesenchymal transition of ovarian cancer by regulating miR–29a–3p/E2F1 axis. Aging (Albany NY) 12(14), 14080–14091 (2020).
Medline CASGoogle Scholar
45. Natanzon Y, Goode EL, Cunningham JM. Epigenetics in ovarian cancer. Semin. Cancer Biol. 51, 160–169 (2018).
Medline CASGoogle Scholar
46. Mohamed NE, Hay T, Reed KR, Smalley MJ, Clarke AR. APC2 is critical for ovarian WNT signalling control, fertility and tumour suppression. BMC Cancer 19, 677 (2019).
MedlineGoogle Scholar

Vol. 16, No. 34

Supplemental Materials

Metrics

Downloaded 107 times

History

Received 29 June 2020

Accepted 17 August 2020

Published online 4 September 2020

Published in print December 2020

Information

Keywords

Supplementary data

To view the supplementary data that accompany this paper please visit the journal website at: www.futuremedicine.com/doi/suppl/10.2217/fon-2020-0668

Author contributions

Conceptualization and writing, S Ma; data analysis, S Ma and Y Zheng; investigation, S Ma, C Fei and Y Zheng; methodology, S Ma and Y Zheng; data analysis, writing and manuscript editing, C Fei; all authors have read and agreed to the published version of the manuscript.

Financial & competing interests disclosure

This paper is supported by the National Natural Science Foundation of China (grant no. 51975124) and Research Start-up Funding of Fudan University (grant no. FDU38341). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

The authors state that they have obtained appropriate institutional review board approval or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved.

PDF download