Skip to main content
SpringerLink
Log in

Utilize Imputation Method and Meta-analysis to Identify DNA-Methylation-Mediated microRNAs in Ovarian Cancer

  • Conference paper
  • First Online:
Algorithms for Computational Biology (AlCoB 2018)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10849))

Included in the following conference series:

Abstract

Both epigenetics and genetic alterations are associated with cancer formation. Identification of prognostic biomarkers, DNA-methylation-mediated miRNAs, is an important step towards developing therapeutic treatment of cancer. Ovarian cancer is one of the most lethal cancers among the females, it was selected for the present study. The TCGA database provides large volume of data for cancer study, which is useful if one can combine different batches of datasets; hence, higher confident results can be obtained. There are several issues arise in data integration, i.e., missing data problem, data heterogeneity problem and the need of construct an automatic platform to reduce human intervention.

Method. Both the normal and ovarian tumor datasets were obtained from the TCGA database. To interpolate the missing methylation values, we employed the KNN imputation method. Simulation tests were performed to obtain the optimal k value. We utilized meta-analysis to minimize the heterogeneity problem and derived statistical significant DNA methylation-mediated-miRNA events. Finally, a semi-automatic pipeline was constructed to facilitate the imputation and meta-analysis studies; thus, identify potential epigenetic biomarkers in a more efficient manner.

Results. Both epigenetic- and TF-mediated effects were examined, which allow us to remove false positive events. The methylation-mediated-miRNA pairs identified by our platform are in-line with literature studies.

Conclusion. We have demonstrated that our imputation and meta-analysis pipeline led to better performance and efficiency in detecting methylation-mediated-miRNA pairs. Furthermore, this study reveals the association between aberrant DNA methylation and alternated miRNA expression, which contributes to better knowledge of the role of epigenetics regulation in ovarian cancer formation.

E.B. Wijaya and E. Lim—Equal contribution.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Feinberg, A.P.: Genome-scale approaches to the epigenetics of common human disease. Virchows Arch. Int. J. Pathol. 456(1), 13–21 (2010). https://doi.org/10.1007/s00428-009-0847-2

    Article  Google Scholar 

  2. Jia, Y., Guo, M.: Epigenetic changes in colorectal cancer. Chin. J. Cancer 32(1), 21–30 (2013). https://doi.org/10.5732/cjc.011.10245

    Article  Google Scholar 

  3. Monteiro, F.L., Vitorino, R., Wang, J., et al.: The histone H2A isoform Hist2h2ac is a novel regulator of proliferation and epithelial–mesenchymal transition in mammary epithelial and in breast cancer cells. Cancer Lett. 396, 42–52 (2017). https://doi.org/10.1016/j.canlet.2017.03.007

    Article  Google Scholar 

  4. Zhang, X., Dong, J., He, Y., et al.: miR-218 inhibited tumor angiogenesis by targeting ROBO1 in gastric cancer. Gene 615, 42–49 (2017)

    Article  Google Scholar 

  5. U.S. Cancer Statistics Working Group: United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-based Report. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute, Atlanta, 11 February 2017. www.cdc.gov/uscs

  6. Lujambio, A., Ropero, S., Ballestar, E., et al.: Genetic unmasking of an epigenetically silenced microRNA in human cancer cells. Cancer Res. 67, 1424–1429 (2007). https://doi.org/10.1158/0008-5472.can-06-4218

    Article  Google Scholar 

  7. Monroig, P.C., Calin, G.A.: MicroRNA and epigenetics: diagnostic and therapeutic opportunities. Curr. Pathobiol. Rep. 1(1), 43–52 (2013)

    Article  Google Scholar 

  8. Agustriawan, D., Huang, C.-H., Sheu, J.J.-C., et al.: DNA methylation-mediatedmicroRNA pathways in ovarian serous cystadenocarcinoma: a meta-analysis. Comput. Biol. Chem. 65, 154–164 (2016)

    Article  Google Scholar 

  9. Biagioni, F., Ben-Noshe, N.B., Fontemaggi, G., et al.: miR-10b, a master inhibitor of the cell cycle, is down-regulated in human breast tumours. EMBO Mol. Med. 4(11), 1214–1229 (2012). https://doi.org/10.1002/emmm.201201483

    Article  Google Scholar 

  10. Biagioni, F., Bossel, B.-M.N., Fontemaggi, G., et al.: The locus of microRNA-10b: a critical target for breast cancer insurgence and dissemination. Cell Cycle 12(15), 2371–2375 (2013). https://doi.org/10.4161/cc.25380

    Article  Google Scholar 

  11. Chen, K., Liu, M.X., Mak, C.S.-L., et al.: Methylation-associated silencing of miR-193a-3p promotes ovarian cancer aggressiveness by targeting GRB7 and MAPK/ERK pathways. Theranostics 8(2), 423–436 (2018)

    Article  Google Scholar 

  12. Delfino, K.R., Rodriguez-Zas, S.L.: Transcription factor-MicroRNA-target gene networks associated with ovarian cancer survival and recurrence. PLoS ONE 8(3), e58608 (2013). https://doi.org/10.1371/journal.pone.0058608

    Article  Google Scholar 

  13. Guo, Y., Sheng, Q., Li, J., et al.: Large scale comparison of gene expression levels by microarrays and RNAseq using TCGA data. PLoS ONE 8(8), e71462 (2013). https://doi.org/10.1371/journal.pone.0071462

    Article  Google Scholar 

  14. Tomczak, K., Czerwińska, P., Wiznerowicz, M.: The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp. Oncol. 19(1A), A68–A77 (2015). https://doi.org/10.5114/wo.2014.47136

    Article  Google Scholar 

  15. Troyanskaya, O., Cantor, M., Sherlock, G., et al.: Missing value estimation methods for DNA microarrays. Bioinformatics 17(6), 520–525 (2001)

    Article  Google Scholar 

  16. Xiang, Q., Dai, X., Deng, Y., et al.: Missing value imputation for microarray gene expression data using histone acetylation information. BMC Bioinform. 9, 252 (2008). https://doi.org/10.1186/1471-2105-9-252

    Article  Google Scholar 

  17. Sung, Y.J., Schwander, K., Arnett, D.K., et al.: An empirical comparison of meta-analysis and mega-analysis of individual participant data for identifying gene-environment interactions. Genet. Epidemiol. 38, 369–378 (2014). https://doi.org/10.1002/gepi.21800

    Article  Google Scholar 

  18. Batista, G.E.A.P.A., Monard, M.C.: A Study of K-Nearest Neighbor as an Imputation Method. HIS, University of São Paulo – USP, Brazil (2002)

    Google Scholar 

  19. Ahmadi-Nedushan, B.: An optimized instance based learning algorithm for estimation of compressive strength of concrete. Eng. Appl. Artif. Intell. 25(5), 1073–1081 (2012)

    Article  Google Scholar 

  20. Kurum, E., Benayoun, B.A., Malhotra, A., et al.: Computational inference of a genomic pluripotency signature in human and mouse stem cells. Biol. Dir. 11, 47 (2016). https://doi.org/10.1186/s13062-016-0148-z

    Article  Google Scholar 

  21. Chien, C.-H., Sun, Y.-M., Chang, W.-C., et al.: Identifying transcriptional start sites of human microRNAs based on high-throughput sequencing data. Nucleic Acids Res. 39(21), 9345–9356 (2011). https://doi.org/10.1093/nar/gkr604

    Article  Google Scholar 

  22. Griffiths-Jones, S., Grocock, R.J., van Dongen, S., et al.: miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 34(Database issue), D140–D144 (2006). https://doi.org/10.1093/nar/gkj112

    Article  Google Scholar 

  23. Borenstein, M., Hedges, L.V., Higgins, J.P., Rothstein, H.R.: Introduction to Meta-Analysis. Wiley, Chichester (2009)

    Book  Google Scholar 

  24. Sung, H.Y., Yang, S.-D., Ju, W., Ahn, J.-H.: Aberrant epigenetic regulation of GABRP associates with aggressive phenotype of ovarian cancer. Exp. Mol. Med. 49, e335 (2017). https://doi.org/10.1038/emm.2017.62

    Article  Google Scholar 

  25. Wang, J., Lu, M., Qiu, C., Cui, Q.: TransmiR: a transcription factor–microRNA regulation database. Nucleic Acids Res. 38(Database issue), D119–D122 (2010). https://doi.org/10.1093/nar/gkp803

    Article  Google Scholar 

  26. Ruepp, A., Kowarsch, A., Schmidl, D., et al.: PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes. Genome Biol. 11(1), R6 (2010). https://doi.org/10.1186/gb-2010-11-1-r6

    Article  Google Scholar 

  27. Jiang, Q., Wang, Y., Hao, Y., et al.: miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res. 37(Database issue), D98–D104 (2009). https://doi.org/10.1093/nar/gkn714

    Article  Google Scholar 

  28. Ehrlich, M.: DNA hypomethylation in cancer cells. Epigenomics 1(2), 239–259 (2009). https://doi.org/10.2217/epi.09.33

    Article  Google Scholar 

  29. Zhang, W., Barger, C.J., Eng, K.H., et al.: PRAME expression and promoter hypomethylation in epithelial ovarian cancer. Oncotarget 7(29), 45352–45369 (2016)

    Google Scholar 

  30. Armstrong, L.: Epigenetics. Garland Science, New York (2013)

    Google Scholar 

  31. Laios, A., O’Toole, S., Flavin, R., et al.: Potential role of miR-9 and miR-223 in recurrent ovarian cancer. Mol. Cancer 7, 35 (2008). https://doi.org/10.1186/1476-4598-7-35

    Article  Google Scholar 

  32. Shell, S., Park, S.-M., Radjabi, A.R., et al.: Let-7 expression defines two differentiation stages of cancer. Proc. Natl. Acad. Sci. U.S.A. 104(27), 11400–11405 (2007). https://doi.org/10.1073/pnas.0704372104

    Article  Google Scholar 

Download references

Acknowledgments

Dr. Chien-Hung Huang work is supported by the Ministry of Science and Technology (MOST) under the grant of MOST 106-2221-E-150-056. Dr. Ka-Lok Ng work is supported by the grants of MOST 106-2221-E-468-017, MOST 106-2632-E-468-002, and also supported under the grant from Asia University, 105-asia-1, and 106-asia-06. Dr. Jeffrey J. P. Tsai work is supported by the grant of MOST 106-2632-E-468-002. Mr. Ezra B. Wijaya work is supported by the grant of MOST 106-2221-E-468 -017.

Author information

Authors and Affiliations

  1. Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan

    Ezra B. Wijaya, Erwandy Lim, Jeffrey J. P. Tsai & Ka-Lok Ng

  2. Department of Bioinformatics, Indonesia International Institute for Life Sciences, East Jakarta, Indonesia

    David Agustriawan

  3. Department of Computer Science and Information Engineering, National Formosa University, Huwei, Taiwan

    Chien-Hung Huang

  4. Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan

    Ka-Lok Ng

Authors
  1. Ezra B. Wijaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Erwandy Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David Agustriawan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chien-Hung Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jeffrey J. P. Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ka-Lok Ng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ka-Lok Ng .

Editor information

Editors and Affiliations

  1. The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Jesper Jansson

  2. Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

  3. University of Extremadura, Cáceres, Spain

    Miguel A. Vega-Rodríguez

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wijaya, E.B., Lim, E., Agustriawan, D., Huang, CH., Tsai, J.J.P., Ng, KL. (2018). Utilize Imputation Method and Meta-analysis to Identify DNA-Methylation-Mediated microRNAs in Ovarian Cancer. In: Jansson, J., Martín-Vide, C., Vega-Rodríguez, M. (eds) Algorithms for Computational Biology. AlCoB 2018. Lecture Notes in Computer Science(), vol 10849. Springer, Cham. https://doi.org/10.1007/978-3-319-91938-6_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-91938-6_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91937-9

  • Online ISBN: 978-3-319-91938-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation