Genomics & Informatics

Korea Genome Organization (한국유전체학회)
권호 표지
DOI QR코드

DOI QR Code

Integrated bioinformatics analysis of validated and circulating miRNAs in ovarian cancer

  • Dogan, Berkcan (Department of Medical Genetics, Faculty of Medicine, Bursa Uludag University) ;
  • Gumusoglu, Ece (Department of Molecular Biology and Genetics, Faculty of Science, Istanbul University) ;
  • Ulgen, Ege (Department of Biostatistics and Medical Informatics, School of Medicine, Acibadem Mehmet Ali Aydinlar University) ;
  • Sezerman, Osman Ugur (Department of Biostatistics and Medical Informatics, School of Medicine, Acibadem Mehmet Ali Aydinlar University) ;
  • Gunel, Tuba (Department of Molecular Biology and Genetics, Faculty of Science, Istanbul University)
  • Received : 2021.11.04
  • Accepted : 2022.06.03
  • Published : 2022.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Recent studies have focused on the early detection of ovarian cancer (OC) using tumor materials by liquid biopsy. The mechanisms of microRNAs (miRNAs) to impact OC and signaling pathways are still unknown. This study aims to reliably perform functional analysis of previously validated circulating miRNAs' target genes by using pathfindR. Also, overall survival and pathological stage analyses were evaluated with miRNAs' target genes which are common in the The Cancer Genome Atlas and GTEx datasets. Our previous studies have validated three downregulated miRNAs (hsa-miR-885-5p, hsa-miR-1909-5p, and hsa-let7d-3p) having a diagnostic value in OC patients' sera, with high-throughput techniques. The predicted target genes of these miRNAs were retrieved from the miRDB database (v6.0). Active-subnetwork-oriented Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted by pathfindR using the target genes. Enrichment of KEGG pathways assessed by the analysis of pathfindR indicated that 24 pathways were related to the target genes. Ubiquitin-mediated proteolysis, spliceosome and Notch signaling pathway were the top three pathways with the lowest p-values (p < 0.001). Ninety-three common genes were found to be differentially expressed (p < 0.05) in the datasets. No significant genes were found to be significant in the analysis of overall survival analyses, but 24 genes were found to be significant with pathological stages analysis (p < 0.05). The findings of our study provide in-silico evidence that validated circulating miRNAs' target genes and enriched pathways are related to OC and have potential roles in theranostics applications. Further experimental investigations are required to validate our results which will ultimately provide a new perspective for translational applications in OC management.

Keywords

Acknowledgement

This work was supported by the Istanbul University Scientific Research Projects Department under grant (grant number: 24059). We thank TUBITAK for the scholarship E.U receives (grant number: 118C039).

References

  1. Alshamrani AA. Roles of microRNAs in ovarian cancer tumorigenesis: two decades later, what have we learned? Front Oncol 2020;10:1084. https://doi.org/10.3389/fonc.2020.01084
  2. Li Y, Wang Q, Ning N, Tang F, Wang Y. Bioinformatic analysis reveals MIR502 as a potential tumour suppressor in ovarian cancer. J Ovarian Res 2020;13:77. https://doi.org/10.1186/s13048-020-00683-y
  3. Ferreira P, Roela RA, Lopez RV, Del Pilar Estevez-Diz M. The prognostic role of microRNA in epithelial ovarian cancer: a systematic review of literature with an overall survival meta-analysis. Oncotarget 2020;11:1085-1095. https://doi.org/10.18632/oncotarget.27246
  4. Zhou Y, Layton O, Hong L. Identification of genes and pathways involved in ovarian epithelial cancer by bioinformatics analysis. J Cancer 2018;9:3016-3022. https://doi.org/10.7150/jca.26133
  5. Bjerke GA, Yi R. Integrated analysis of directly captured microRNA targets reveals the impact of microRNAs on mammalian transcriptome. RNA 2020;26:306-323. https://doi.org/10.1261/rna.073635.119
  6. Coban N, Pirim D, Erkan AF, Dogan B, Ekici B. Hsa-miR-584-5p as a novel candidate biomarker in Turkish men with severe coronary artery disease. Mol Biol Rep 2020;47:1361-1369. https://doi.org/10.1007/s11033-019-05235-2
  7. Svoronos AA, Engelman DM, Slack FJ. OncomiR or tumor sup pressor? The duplicity of microRNAs in cancer. Cancer Res 2016;76:3666-3670. https://doi.org/10.1158/0008-5472.CAN-16-0359
  8. Staicu CE, Predescu DV, Rusu CM, Radu BM, Cretoiu D, Suciu N, et al. Role of microRNAs as clinical cancer biomarkers for ovarian cancer: a short overview. Cells 2020;9:169. https://doi.org/10.3390/cells9010169
  9. Palma Flores C, Garcia-Vazquez R, Gallardo Rincon D, Ruiz-Garcia E, Astudillo de la Vega H, Marchat LA, et al. MicroRNAs driving invasion and metastasis in ovarian cancer: opportunities for translational medicine (Review). Int J Oncol 2017;50:1461-1476. https://doi.org/10.3892/ijo.2017.3948
  10. He B, Zhao Z, Cai Q, Zhang Y, Zhang P, Shi S, et al. miRNA-based biomarkers, therapies, and resistance in Cancer. Int J Biol Sci 2020;16:2628-2647. https://doi.org/10.7150/ijbs.47203
  11. Otoukesh B, Abbasi M, Gorgani HO, Farahini H, Moghtadaei M, Boddouhi B, et al. MicroRNAs signatures, bioinformatics analysis of miRNAs, miRNA mimics and antagonists, and miRNA therapeutics in osteosarcoma. Cancer Cell Int 2020;20:254. https://doi.org/10.1186/s12935-020-01342-4
  12. Chen L, Heikkinen L, Wang C, Yang Y, Sun H, Wong G. Trends in the development of miRNA bioinformatics tools. Brief Bioinform 2019;20:1836-1852. https://doi.org/10.1093/bib/bby054
  13. Ulgen E, Ozisik O, Sezerman OU. pathfindR: an R package for comprehensive identification of enriched pathways in omics data through active subnetworks. Front Genet 2019;10:858. https://doi.org/10.3389/fgene.2019.00858
  14. Dogan B. Selection of biomarker from candidate miRNAs for early detection of ovarian cancer. M.S. Thesis. Istanbul: Istanbul University, Council of Higher Education, 2018.
  15. Gumusoglu E. Molecular diagnosis and surveillance in ovarian cancer by miRNA analysis. M.S. Thesis. Istanbul: Istanbul University, Council of Higher Education, 2016.
  16. Chen Y, Wang X. miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res 2020;48:D127-D131. https://doi.org/10.1093/nar/gkz757
  17. Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 2017;45:W98-W102. https://doi.org/10.1093/nar/gkx247
  18. Peng Y, Croce CM. The role of MicroRNAs in human cancer. Signal Transduct Target Ther 2016;1:15004. https://doi.org/10.1038/sigtrans.2015.4
  19. Wang H, Peng R, Wang J, Qin Z, Xue L. Circulating microRNAs as potential cancer biomarkers: the advantage and disadvantage. Clin Epigenetics 2018;10:59. https://doi.org/10.1186/s13148-018-0492-1
  20. Tokar T, Pastrello C, Rossos AE, Abovsky M, Hauschild AC, Tsay M, et al. mirDIP 4.1-integrative database of human microRNA target predictions. Nucleic Acids Res 2018;46:D360-D370. https://doi.org/10.1093/nar/gkx1144
  21. Dogan B, Ayar B, Pirim D. miRNA discovery and current in silico approaches in clinical research. In: Current Medical Biology and Genetics Studies (Hu L, ed.). Ankara: Akademisyen Kitabevi, 2020. pp. 1-20.
  22. Soave CL, Guerin T, Liu J, Dou QP. Targeting the ubiquitin-proteasome system for cancer treatment: discovering novel inhibitors from nature and drug repurposing. Cancer Metastasis Rev 2017;36:717-736. https://doi.org/10.1007/s10555-017-9705-x
  23. Shen M, Schmitt S, Buac D, Dou QP. Targeting the ubiquitin-proteasome system for cancer therapy. Expert Opin Ther Targets 2013;17:1091-1108. https://doi.org/10.1517/14728222.2013.815728
  24. Rao Z, Ding Y. Ubiquitin pathway and ovarian cancer. Curr Oncol 2012;19:324-328. https://doi.org/10.3747/co.19.1175
  25. Bazzaro M, Lee MK, Zoso A, Stirling WL, Santillan A, Shih Ie M, et al. Ubiquitin-proteasome system stress sensitizes ovarian cancer to proteasome inhibitor-induced apoptosis. Cancer Res 2006;66:3754-3763. https://doi.org/10.1158/0008-5472.CAN-05-2321
  26. Wang E, Aifantis I. RNA splicing and cancer. Trends Cancer 2020;6:631-644. https://doi.org/10.1016/j.trecan.2020.04.011
  27. Li Y, Guo H, Jin C, Qiu C, Gao M, Zhang L, et al. Spliceosome-associated factor CTNNBL1 promotes proliferation and invasion in ovarian cancer. Exp Cell Res 2017;357:124-134. https://doi.org/10.1016/j.yexcr.2017.05.008
  28. da Silva MR, Moreira GA, Goncalves da Silva RA, de Almeida Alves Barbosa E, Pais Siqueira R, Teixera RR, et al. Splicing regulators and their roles in cancer biology and therapy. Biomed Res Int 2015;2015:150514.
  29. El Marabti E, Younis I. The cancer spliceome: reprograming of alternative splicing in cancer. Front Mol Biosci 2018;5:80. https://doi.org/10.3389/fmolb.2018.00080
  30. Jia D, Underwood J, Xu Q, Xie Q. NOTCH2/NOTCH3/DLL3/ MAML1/ADAM17 signaling network is associated with ovarian cancer. Oncol Lett 2019;17:4914-4920.
  31. Groeneweg JW, Foster R, Growdon WB, Verheijen RH, Rueda BR. Notch signaling in serous ovarian cancer. J Ovarian Res 2014;7:95. https://doi.org/10.1186/s13048-014-0095-1
  32. Chiaramonte R, Colombo M, Bulfamante G, Falleni M, Tosi D, Garavelli S, et al. Notch pathway promotes ovarian cancer growth and migration via CXCR4/SDF1alpha chemokine system. Int J Biochem Cell Biol 2015;66:134-140. https://doi.org/10.1016/j.biocel.2015.07.015
  33. Nagy A, Munkacsy G, Gyorffy B. Pancancer survival analysis of cancer hallmark genes. Sci Rep 2021;11:6047. https://doi.org/10.1038/s41598-021-84787-5
  34. Yoshihara K, Tajima A, Yahata T, Kodama S, Fujiwara H, Suzuki M, et al. Gene expression profile for predicting survival in advanced-stage serous ovarian cancer across two independent datasets. PLoS One 2010;5:e9615. https://doi.org/10.1371/journal.pone.0009615
  35. Cosma G, Acampora G, Brown D, Rees RC, Khan M, Pockley AG. Prediction of pathological stage in patients with prostate cancer: a neuro-fuzzy model. PLoS One 2016;11:e0155856. https://doi.org/10.1371/journal.pone.0155856
  36. Ren CM, Liang Y, Wei F, Zhang YN, Zhong SQ, Gu H, et al. Balanced translocation t(3;18)(p13;q22.3) and points mutation in the ZNF407 gene detected in patients with both moderate non-syndromic intellectual disability and autism. Biochim Biophys Acta 2013;1832:431-438. https://doi.org/10.1016/j.bbadis.2012.11.009
  37. Tan X, Chen S, Wu J, Lin J, Pan C, Ying X, et al. PI3K/AKT-mediated upregulation of WDR5 promotes colorectal cancer metastasis by directly targeting ZNF407. Cell Death Dis 2017;8:e2686. https://doi.org/10.1038/cddis.2017.111
  38. Lupo J, Conti A, Sueur C, Coly PA, Coute Y, Hunziker W, et al. Identification of new interacting partners of the shuttling protein ubinuclein (Ubn-1). Exp Cell Res 2012;318:509-520. https://doi.org/10.1016/j.yexcr.2011.12.020
  39. Zhao YL, Zhong SR, Zhang SH, Bi JX, Xiao ZY, Wang SY, et al. UBN2 promotes tumor progression via the Ras/MAPK pathway and predicts poor prognosis in colorectal cancer. Cancer Cell Int 2019;19:126. https://doi.org/10.1186/s12935-019-0848-4