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RNA Mapping pp 279–288Cite as

Tapping MicroRNA Regulation Networks Through Integrated Analysis of MicroRNA–mRNA High-Throughput Profiles

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1182))

Abstract

Understanding the biological relevance and context of microRNA (miRNA) regulation of target mRNAs is difficult to ascertain because an individual miRNA aids simultaneously in the regulation of hundreds of mRNAs in a cell. With the increasing availability of large public datasets that profile both mRNA and miRNA expression levels from the same samples, it is possible to apply robust statistical methods to identify global negative correlations in miRNA and target mRNA expression. Using a dataset from The Cancer Genome Atlas as a case study, we show how to use linear regression analysis followed by permutation-based false discovery rate to assign high statistical power to pair-wise negative correlations of miRNA and mRNA expression. Used in conjunction with available prediction tools or other target databases, a high confidence dataset of global miRNA–mRNA interactions can be generated. We also describe further methods to prioritize identified interactions by integrating with mutation, copy number variation, methylation, or survival data to support observations and provide context. Finally, we discuss methods to experimentally validate selected novel targets.

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References

  1. Lim LP, Lau NC, Garrett-Engele P et al (2005) Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433:769–773

    Article  CAS  PubMed  Google Scholar 

  2. Witkos TM, Koscianska E, Krzyzosiak WJ (2011) Practical aspects of microRNA target prediction. Curr Mol Med 11:93–109

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  3. Muniategui A, Pey J, Planes FJ et al (2013) Joint analysis of miRNA and mRNA expression data. Brief Bioinform 14:263–278

    Article  CAS  PubMed  Google Scholar 

  4. Le TD, Liu L, Liu B et al (2013) Inferring microRNA and transcription factor regulatory networks in heterogeneous data. BMC Bioinformatics 14:92

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  5. Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136:215–233

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  6. Brazma A, Parkinson H, Sarkans U et al (2003) ArrayExpress–a public repository for microarray gene expression data at the EBI. Nucleic Acids Res 31:68–71

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  7. Edgar R, Domrachev M, Lash AE (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30:207–210

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  8. Kuo TY, Hsi E, Yang IP et al (2012) Computational analysis of mRNA expression profiles identifies microRNA-29a/c as predictor of colorectal cancer early recurrence. PLoS One 7:e31587

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  9. Network CGA (2012) Comprehensive molecular portraits of human breast tumours. Nature 490:61–70

    Article  Google Scholar 

  10. Wang K, Singh D, Zeng Z et al (2010) MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res 38:e178

    Article  PubMed Central  PubMed  Google Scholar 

  11. Li B, Dewey C (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12:323

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. Robinson MD, Oshlack A (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11:R25

    Article  PubMed Central  PubMed  Google Scholar 

  13. Garmire LX, Subramaniam S (2012) Evaluation of normalization methods in mammalian microRNA-Seq data. RNA 18:1279–1288

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  14. Kadota K, Nishiyama T, Shimizu K (2012) A normalization strategy for comparing tag count data. Algorithms Mol Biol 7:5

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  15. Hackstadt AJ, Hess AM (2009) Filtering for increased power for microarray data analysis. BMC Bioinformatics 10:11

    Article  PubMed Central  PubMed  Google Scholar 

  16. Bourgon R, Gentleman R, Huber W (2010) Independent filtering increases detection power for high-throughput experiments. Proc Natl Acad Sci U S A 107:9546–9551

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  17. Barbato C, Arisi I, Frizzo ME et al (2009) Computational challenges in miRNA target predictions: to be or not to be a true target? J Biomed Biotechnol 2009:803069

    PubMed Central  PubMed  Google Scholar 

  18. Wang X, Wang X (2006) Systematic identification of microRNA functions by combining target prediction and expression profiling. Nucleic Acids Res 34:1646–1652

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. Gennarino VA, Sardiello M, Avellino R et al (2009) MicroRNA target prediction by expression analysis of host genes. Genome Res 19:481–490

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  20. Huang JC, Babak T, Corson TW et al (2007) Using expression profiling data to identify human microRNA targets. Nat Methods 4:1045–1049

    Article  CAS  PubMed  Google Scholar 

  21. Xiao F, Zuo Z, Cai G et al (2009) miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res 37(Database issue):D105–D110

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  22. Hsu SD, Lin FM, Wu WY et al (2011) miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res 39(Database issue):D163–D169

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  23. Broad Institute TCGA Genome Data Analysis Center (2013) Analysis overview for head and neck squamous cell carcinoma (Primary solid tumor cohort). Broad Institute of MIT and Harvard. doi: 10.7908/C1W]66HQ3. Accessed on 21 Apr, 2013.

    Google Scholar 

  24. Birney E, Stamatoyannopoulos JA, Dutta A et al (2007) Identification and analysis of functional elements in 1 % of the human genome by the ENCODE pilot project. Nature 447:799–816

    Article  CAS  PubMed  Google Scholar 

  25. Kent WJ, Sugnet CW, Furey TS et al (2002) The human genome browser at UCSC. Genome Res 12:996–1006

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  26. Robinson JT, Thorvaldsdottir H, Winckler W et al (2011) Integrative genomics viewer. Nat Biotechnol 29:24–26

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  27. West JA, Viswanathan SR, Yabuuchi A et al (2009) A role for Lin28 in primordial germ-cell development and germ-cell malignancy. Nature 460:909–913

    CAS  PubMed Central  PubMed  Google Scholar 

  28. Lu J, Getz G, Miska EA et al (2005) MicroRNA expression profiles classify human cancers. Nature 435:834–838

    Article  CAS  PubMed  Google Scholar 

  29. Iorio MV, Ferracin M, Liu CG et al (2005) MicroRNA gene expression deregulation in human breast cancer. Cancer Res 65:7065–7070

    Article  CAS  PubMed  Google Scholar 

  30. Shell S, Park SM, Radjabi AR et al (2007) Let-7 expression defines two differentiation stages of cancer. Proc Natl Acad Sci U S A 104:11400–11405

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  31. Team RC (2012) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  32. Esquela-Kerscher A, Trang P, Wiggins JF et al (2008) The let-7 microRNA reduces tumor growth in mouse models of lung cancer. Cell Cycle 7:759–764

    Article  CAS  PubMed  Google Scholar 

  33. Boyerinas B, Park SM, Hau A et al (2010) The role of let-7 in cell differentiation and cancer. Endocr Relat Cancer 17:F19–F36

    Article  CAS  PubMed  Google Scholar 

  34. Akao Y, Nakagawa Y, Naoe T (2006) let-7 microRNA functions as a potential growth suppressor in human colon cancer cells. Biol Pharm Bull 29:903–906

    Article  CAS  PubMed  Google Scholar 

  35. Nadiminty N, Tummala R, Lou W et al (2012) MicroRNA let-7c is downregulated in prostate cancer and suppresses prostate cancer growth. PLoS One 7:e32832

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  36. Nunez-Iglesias J, Liu CC, Morgan TE et al (2010) Joint genome-wide profiling of miRNA and mRNA expression in Alzheimer’s disease cortex reveals altered miRNA regulation. PLoS One 5:e8898

    Article  PubMed Central  PubMed  Google Scholar 

  37. Wang YP, Li KB (2009) Correlation of expression profiles between microRNAs and mRNA targets using NCI-60 data. BMC Genomics 10:218

    Article  PubMed Central  PubMed  Google Scholar 

  38. Liu H, D’Andrade P, Fulmer-Smentek S et al (2010) mRNA and microRNA expression profiles of the NCI-60 integrated with drug activities. Mol Cancer Ther 9:1080–1091

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  39. Hassan MQ, Gordon JA, Lian JB et al (2010) Ribonucleoprotein immunoprecipitation (RNP-IP): a direct in vivo analysis of microRNA-targets. J Cell Biochem 110:817–822

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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Author notes

    Authors and Affiliations

    1. Tumor Biology Section, Head and Neck Surgery Branch, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, 10 Center Drive Rm 5D55, Bethesda, MD, USA

      Anthony D. Saleh & Hui Cheng

    Authors
    1. Anthony D. Saleh
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    2. Hui Cheng
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    Correspondence to Anthony D. Saleh .

    Editor information

    Editors and Affiliations

    1. Diabetes, Cardiovascular, and Metabolic Diseases, Translational Genomics Research Institute, Phoenix, Arizona, USA

      M. Lucrecia Alvarez

    2. Department of Plastic Surgery, University Hospital of the RWTH Aachen, Germany

      Mahtab Nourbakhsh

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    Saleh, A.D., Cheng, H. (2014). Tapping MicroRNA Regulation Networks Through Integrated Analysis of MicroRNA–mRNA High-Throughput Profiles. In: Alvarez, M., Nourbakhsh, M. (eds) RNA Mapping. Methods in Molecular Biology, vol 1182. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1062-5_24

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    • DOI: https://doi.org/10.1007/978-1-4939-1062-5_24

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    • Publisher Name: Humana Press, New York, NY

    • Print ISBN: 978-1-4939-1061-8

    • Online ISBN: 978-1-4939-1062-5

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