Identification of Genes and MicroRNAs Involved in Ovarian Carcinogenesis

  • Wan, Shu-Mei (Department of Gynecology, General Hospital of Guangzhou Military Command of PLA) ;
  • Lv, Fang (Department of Gynecology, Bo-ai Hospital) ;
  • Guan, Ting (Department of Gynecology, General Hospital of Guangzhou Military Command of PLA)
  • Published : 2012.08.31


MicroRNAs (miRNAs) play roles in the clinic, both as diagnostic and therapeutic tools. The identification of relevant microRNAs is critically required for ovarian cancer because of the prevalence of late diagnosis and poor treatment options currently. To identify miRNAs involved in the development or progression of ovarian cancer, we analyzed gene expression profiles downloaded from Gene Expression Omnibus. Comparison of expression patterns between carcinomas and the corresponding normal ovarian tissues enabled us to identify 508 genes that were commonly up-regulated and 1331 genes that were down-regulated in the cancer specimens. Function annotation of these genes showed that most of the up-regulated genes were related to cell cycling, and most of the down-regulated genes were associated with the immune response. When these differentially expressed genes were mapped to MiRTarBase, we obtained a total of 18 key miRNAs which may play important regulatory roles in ovarian cancer. Investigation of these genes and microRNAs should help to disclose the molecular mechanisms of ovarian carcinogenesis and facilitate development of new approaches to therapeutic intervention.


MicroRNA;ovarian cancer;differentially expressed genes;gene ontology


Supported by : Guangdong Natural Science Foundation


  1. Bartel DP (2009). MicroRNAs: target recognition and regulatory functions. Cell, 136, 215-33.
  2. Ashburner M, Ball CA, Blake JA, et al (2000). Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 25, 25-9.
  3. Bartel DP (2004). MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 116, 281-97.
  4. Benjamini YH, Y (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J Royal Statist Society Series B (Methodological), 57, 289-300.
  5. Butt AJ, Caldon CE, McNeil CM, et al. (2008). Cell cycle machinery: links with genesis and treatment of breast cancer. Adv Exp Med Biol, 630, 189-205.
  6. Carroll MC (2004). The complement system in regulation of adaptive immunity. Nat Immunol, 5, 981-6.
  7. Colomiere M, Ward AC, Riley C, et al (2009). Cross talk of signals between EGFR and IL-6R through JAK2/STAT3 mediate epithelial-mesenchymal transition in ovarian carcinomas. Br J Cancer, 100, 134-44.
  8. D'Andrilli G, Kumar C, Scambia G, Giordano A (2004). Cell cycle genes in ovarian cancer: steps toward earlier diagnosis and novel therapies. Clin Cancer Res, 10, 8132-41.
  9. Elledge SJ (1996). Cell cycle checkpoints: preventing an identity crisis. Science, 274, 1664-72.
  10. Gautier L, Cope L, Bolstad BM, Irizarry RA (2004). affy-- analysis of Affymetrix GeneChip data at the probe level. Bioinformatics, 20, 307-15.
  11. Holschneider CH, Berek JS (2000). Ovarian cancer: epidemiology, biology, and prognostic factors. Semin Surg Oncol, 19, 3-10.<3::AID-SSU2>3.0.CO;2-S
  12. Hsu SD, Lin FM, Wu WY, et al (2011). miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res, 39, D163-9.
  13. Huang Q, Gumireddy K, Schrier M, et al (2008). The microRNAs miR-373 and miR-520c promote tumour invasion and metastasis. Nat Cell Biol, 10, 202-10.
  14. Iorio MV, Visone R, Di Leva G, et al (2007). MicroRNA signatures in human ovarian cancer. Cancer Res, 67, 8699- 707.
  15. Irizarry RA, Hobbs B, Collin F, et al (2003). Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4, 249-64.
  16. Johnson SM, Grosshans H, Shingara J, et al (2005). RAS is regulated by the let-7 microRNA family. Cell, 120, 635-47.
  17. Kusenda B, Mraz M, Mayer J, Pospisilova S (2006). MicroRNA biogenesis, functionality and cancer relevance. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub, 150, 205-15.
  18. Lee RC, Feinbaum RL, Ambros V (1993). The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell, 75, 843-54.
  19. Lee YS, Dutta A (2007). The tumor suppressor microRNA let-7 represses the HMGA2 oncogene. Genes Dev, 21, 1025-30.
  20. Ma L, Teruya-Feldstein J, Weinberg RA (2007). Tumour invasion and metastasis initiated by microRNA-10b in breast cancer. Nature, 449, 682-8.
  21. Meng F, Henson R, Wehbe-Janek H, et al (2007). MicroRNA-21 regulates expression of the PTEN tumor suppressor gene in human hepatocellular cancer. Gastroenterology, 133, 647-58.
  22. Min H, Yoon S (2010). Got target? Computational methods for microRNA target prediction and their extension. Exp Mol Med, 42, 233-44.
  23. Mok SC, Bonome T, Vathipadiekal V, et al (2009). A gene signature predictive for outcome in advanced ovarian cancer identifies a survival factor: microfibril-associated glycoprotein 2. Cancer Cell, 16, 521-32.
  24. Morgan BP, Marchbank KJ, Longhi MP, Harris CL, Gallimore AM (2005). Complement: central to innate immunity and bridging to adaptive responses. Immunol Lett, 97, 171-9.
  25. Murray KP, Mathure S, Kaul R, et al (2000). Expression of complement regulatory proteins-CD 35, CD 46, CD 55, and CD 59-in benign and malignant endometrial tissue. Gynecol Oncol, 76, 176-82.
  26. Nam EJ, Kim YT (2008). Alteration of cell-cycle regulation in epithelial ovarian cancer. Int J Gynecol Cancer, 18, 1169-82.
  27. Nam EJ, Yoon H, Kim SW, et al. (2008). MicroRNA expression profiles in serous ovarian carcinoma. Clin Cancer Res, 14, 2690-95.
  28. Ono K, Tanaka T, Tsunoda T, et al (2000). Identification by cDNA microarray of genes involved in ovarian carcinogenesis. Cancer Res, 60, 5007-11.
  29. Pharoah PD, Tyrer J, Dunning AM, Easton DF, Ponder BA (2007). Association between common variation in 120 candidate genes and breast cancer risk. PLoS Genet, 3, e42.
  30. Ricklin D, Lambris JD (2007). Complement-targeted therapeutics. Nat Biotechnol, 25, 1265-75.
  31. Team RDC (2011). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.
  32. van Jaarsveld MT, Helleman J, Berns EM, Wiemer EA (2010). MicroRNAs in ovarian cancer biology and therapy resistance. Int J Biochem Cell Biol, 42, 1282-90.
  33. Voorhoeve PM, le Sage C, Schrier M, et al (2006). A genetic screen implicates miRNA-372 and miRNA-373 as oncogenes in testicular germ cell tumors. Cell, 124, 1169-81.
  34. Wang J, Zhou X, Zhu J, et al (2011). GO-function: deriving biologically relevant functions from statistically significant functions. Brief Bioinform.
  35. Wightman B, Ha I, Ruvkun G (1993). Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell, 75, 855-62.
  36. Zhang L, Huang J, Yang N, et al (2006). microRNAs exhibit high frequency genomic alterations in human cancer. Proc Natl Acad Sci USA, 103, 9136-41.

Cited by

  1. miR-186 Regulates Glycolysis through Glut1 During the Formation of Cancer-associated Fibroblasts vol.15, pp.10, 2014,
  2. Lack of Any Association of GST Genetic Polymorphisms with Susceptibility to Ovarian Cancer - a Meta-analysis vol.15, pp.15, 2014,
  3. MicroRNAs: Biogenesis, Roles for Carcinogenesis and as Potential Biomarkers for Cancer Diagnosis and Prognosis vol.15, pp.18, 2014,
  4. Differential microRNA Expression by Solexa Sequencing in the Sera of Ovarian Cancer Patients vol.15, pp.4, 2014,
  5. MiR-506 suppresses proliferation and induces senescence by directly targeting the CDK4/6-FOXM1 axis in ovarian cancer vol.233, pp.3, 2014,
  6. Can Cancer Therapy be Achieved by Bridging Apoptosis and Autophagy: a Method Based on microRNA-Dependent Gene Therapy and Phytochemical Targets vol.16, pp.17, 2015,