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Integration of Omics Data to Identify Cancer-Related MicroRNA

  • Luciano CascioneEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1970)

Abstract

MicroRNAs regulate genes involved in various biological processes and may play oncogenic or tumor suppressive roles. Many studies have investigated the relationships between microRNAs and their target genes using mRNA and microRNA expression data. Integrating different types of molecular data could lead to a better understanding of the regulatory network of disease-causing pathways. For this potential to be fully realized, methods for properly integrating omics data are necessary. Here, the computational methods for addressing these challenges are described, and key considerations for analyzing and interpreting profiling data are discussed.

Key words

miRNA expression Gene expression RNAseq Correlation Integrative analysis 

References

  1. 1.
    Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116(2):281–297CrossRefGoogle Scholar
  2. 2.
    Baek D, Villen J, Shin C, Camargo FD, Gygi SP, Bartel DP (2008) The impact of microRNAs on protein output. Nature 455:64–71CrossRefGoogle Scholar
  3. 3.
    Lim L, Lau N, Garrett-Engele P, Grimson A, Schelter J, Castle J, Bartel DP, Linsley PS, Johnson JM (2005) Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433(7027):769–773CrossRefGoogle Scholar
  4. 4.
    Croce C (2009) Causes and consequences of microRNA dysregulation in cancer. Nat Rev Genet 10(10):704–714CrossRefGoogle Scholar
  5. 5.
    Musilova K, Mraz M (2015) MicroRNAs in B-cell lymphomas: how a complex biology gets more complex. Leukemia 29(5):1004–1017CrossRefGoogle Scholar
  6. 6.
    Cascione L, Gasparini P, Lovat F, Carasi S, Pulvirenti A, Ferro A, Alder H, He G, Vecchione A, Croce CM, Shapiro CL, Huebner K (2013) Integrated microRNA and mRNA signatures associated with survival in triple negative breast cancer. PLoS One 8(2):e55910CrossRefGoogle Scholar
  7. 7.
    Svoronos AA, Engelman DM, Slack FJ (2017) OncomiR or tumor suppressor? The duplicity of MicroRNAs in cancer. Cancer ResGoogle Scholar
  8. 8.
    Li Y, Liang M, Zhang Z (2014) Regression analysis of combined gene expression regulation in acute myeloid leukemia. PLoS Comput Biol 10(10):e1003908CrossRefGoogle Scholar
  9. 9.
    Chen X, Slack FJ, Zhao H (2013) Joint analysis of expression profiles from multiple cancers improves the identification of microRNA-gene interactions. Bioinformatics 29(17):2137–2145CrossRefGoogle Scholar
  10. 10.
    Wang S, Wu W, Claret FX (2017) Mutual regulation of microRNAs and DNA methylation in human cancers. Epigenetics 12(3):187–197CrossRefGoogle Scholar
  11. 11.
    Wei Y (2015) Integrative analyses of cancer data: a review from a statistical perspective. Cancer Inform 14(Suppl 2):173–181PubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Oncology ResearchUniversità della Svizzera ItalianaBellinzonaSwitzerland
  2. 2.Swiss Institute of Bioinformatics (SIB)LausanneSwitzerland

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