Skip to main content
SpringerLink
Log in

Analysis of Paired miRNA-mRNA Microarray Expression Data Using a Stepwise Multiple Linear Regression Model

  • Conference paper
  • First Online:
Bioinformatics Research and Applications (ISBRA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10330))

Included in the following conference series:

Abstract

MicroRNAs are small endogenous RNAs that play important roles in gene regulation. With the accumulation of expression data, numerous approaches have been proposed to infer miRNA-mRNA regulation from paired miRNA-mRNA expression profiles. These mainly focus on discovering and validating the structure of regulatory networks, but do not address the prediction and simulation tasks. Furthermore, functional annotation of miRNAs relies on miRNA target prediction, which is problematic since miRNA-gene interactions are highly tissue-specific. Thus a different approach to functional annotation of miRNA-mRNA regulation that can generate context-specific expression levels is needed. In this study, we analyzed paired miRNA-mRNA expressions from breast cancer studies. The expression of mRNAs is modeled as a multiple linear function of the expression of miRNAs and the parameters are estimated using stepwise multiple linear regression (SMLR). We demonstrate that the SMLR model can predict mRNA expression patterns from miRNA expressions alone and that the predicted gene expression levels preserve differentially regulated gene sets, as well as the functional categories of these genes. We show that our quantitative approach can determine affected biological activities better than the traditional target-prediction based methods.

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 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
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. Bartel, D.P.: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116(2), 281–297 (2004)

    Article  Google Scholar 

  2. Vasudevan, S., Tong, Y., Steitz, J.A.: Switching from repression to activation: micrornas can up-regulate translation. Science 318(5858), 1931–1934 (2007)

    Article  Google Scholar 

  3. Hobert, O.: Gene regulation by transcription factors and microRNAs. Science 319(5871), 1785–1786 (2008)

    Article  Google Scholar 

  4. Fabian, M.R., Sonenberg, N., Filipowicz, W.: Regulation of mRNA translation and stability by microRNAs. Ann. Rev. Biochem. 79(1), 351–379 (2010)

    Article  Google Scholar 

  5. Huntzinger, E., Izaurralde, E.: Gene silencing by microRNAs: contributions of translational repression and mRNA decay. Nat. Rev. Genet. 12(2), 99–110 (2011)

    Article  Google Scholar 

  6. Guo, H., et al.: Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature 466(7308), 835–840 (2010)

    Article  Google Scholar 

  7. Friedman, R.C., et al.: Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 19(1), 92–105 (2009)

    Article  Google Scholar 

  8. Croce, C.M.: Causes and consequences of microRNA dysregulation in cancer. Nat. Rev. Genet. 10(10), 704–714 (2009)

    Article  Google Scholar 

  9. Lujambio, A., Lowe, S.W.: The microcosmos of cancer. Nature 482(7385), 347–355 (2012)

    Article  Google Scholar 

  10. Ørom, U.A., Lund, A.H.: Experimental identification of microRNA targets. Gene 451(1–2), 1–5 (2010)

    Article  Google Scholar 

  11. Vergoulis, T., et al.: TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res. 40, D222–D229 (2011)

    Google Scholar 

  12. Hsu, S.D., et al.: miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. Nucleic Acids Res. 42(Database issue), D78–D85 (2014)

    Google Scholar 

  13. John, B., et al.: Human microRNA targets. PLoS Biol. 2(11), e363 (2004)

    Article  Google Scholar 

  14. Krek, A., et al.: Combinatorial microRNA target predictions. Nat. Genet. 37(5), 495–500 (2005)

    Article  Google Scholar 

  15. Lewis, B.P., Burge, C.B., Bartel, D.P.: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120(1), 15–20 (2005)

    Article  Google Scholar 

  16. Maragkakis, M., et al.: DIANA-microT web server: elucidating microRNA functions through target prediction. Nucleic Acids Res. 37(Web Server issue), W273–W276 (2009)

    Google Scholar 

  17. Sethupathy, P., Megraw, M., Hatzigeorgiou, A.G.: A guide through present computational approaches for the identification of mammalian microRNA targets. Nat. Methods 3(11), 881–886 (2006)

    Article  Google Scholar 

  18. Pritchard, C.C., Cheng, H.H., Tewari, M.: MicroRNA profiling: approaches and considerations. Nat. Rev. Genet. 13(5), 358–369 (2012)

    Article  Google Scholar 

  19. Nam, S., et al.: miRGator: an integrated system for functional annotation of microRNAs. Nucleic Acids Res. 36(Suppl. 1), D159–D164 (2008)

    Google Scholar 

  20. Huang, G.T., Athanassiou, C., Benos, P.V.: mirConnX: condition-specific mRNA-microRNA network integrator. Nucleic Acids Res. 39(Suppl. 2), W416–W423 (2011)

    Article  Google Scholar 

  21. Ritchie, W., Flamant, S., Rasko, J.E.J.: mimiRNA: a microRNA expression profiler and classification resource designed to identify functional correlations between microRNAs and their targets. Bioinformatics 26(2), 223–227 (2010)

    Article  Google Scholar 

  22. Peng, X., et al.: Computational identification of hepatitis C virus associated microRNA-mRNA regulatory modules in human livers. BMC Genom. 10(1), 373 (2009)

    Article  Google Scholar 

  23. Sales, G., et al.: MAGIA, a web-based tool for miRNA and genes integrated analysis. Nucleic Acids Res. 38(Suppl. 2), W352–W359 (2010)

    Article  Google Scholar 

  24. Nam, S., et al.: MicroRNA and mRNA integrated analysis (MMIA): a web tool for examining biological functions of microRNA expression. Nucleic Acids Res. 37(Suppl. 2), W356–W362 (2009)

    Article  Google Scholar 

  25. Kim, S., Choi, M., Cho, K.H.: Identifying the target mRNAs of microRNAs in colorectal cancer. Comput. Biol. Chem. 33(1), 94–99 (2009)

    Article  MATH  Google Scholar 

  26. Wang, H., Li, W.H.: Increasing MicroRNA target prediction confidence by the relative R(2) method. J. Theoret. Biol. 259(4), 793–798 (2009)

    Article  MathSciNet  Google Scholar 

  27. Beck, D., et al.: Integrative analysis of next generation sequencing for small non-coding RNAs and transcriptional regulation in myelodysplastic syndromes. BMC Med. Genom. 4(1), 19 (2011)

    Article  Google Scholar 

  28. Huang, J.C., Morris, Q.D., Frey, B.J.: Bayesian inference of MicroRNA targets from sequence and expression data. J. Comput. Biol. 14(5), 550–563 (2007)

    Article  MathSciNet  Google Scholar 

  29. Huang, J.C., Frey, B.J., Morris, Q.D.: Comparing sequence and expression for predicting microRNA targets using GenMiR3. In: Pacific Symposium on Biocomputing, pp. 52–63 (2008)

    Google Scholar 

  30. Su, N., et al.: Predicting microRNA targets by integrating sequence and expression data in cancer. In: 2011 IEEE International Conference on Systems Biology (ISB) (2011)

    Google Scholar 

  31. Stingo, F.C., et al.: A Bayesian graphical modeling approach to microRNA regulatory network inference. Ann. Appl. Stat. 4(4), 2024–2048 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  32. Liu, B., et al.: Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy. BMC Bioinform. 10(1), 408 (2009)

    Article  Google Scholar 

  33. Zhou, Y., Qureshi, R., Sacan, A.: Data simulation and regulatory network reconstruction from time-series microarray data using stepwise multiple linear regression. Netw. Model. Anal. Health Inform. Bioinform. 1(1–2), 3–17 (2012)

    Article  Google Scholar 

  34. Liu, B., Li, J., Cairns, M.J.: Identifying miRNAs, targets and functions. Briefings Bioinform. 15(1), 1–19 (2014)

    Article  Google Scholar 

  35. da Huang, W., Sherman, B.T., Lempicki, R.A.: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4(1), 44–57 (2009)

    Article  Google Scholar 

  36. Enerly, E., et al.: miRNA-mRNA integrated analysis reveals roles for miRNAs in primary breast tumors. PLoS ONE 6(2), e16915 (2011)

    Article  Google Scholar 

  37. Buffa, F.M., et al.: microRNA-associated progression pathways and potential therapeutic targets identified by integrated mRNA and microRNA expression profiling in breast cancer. Cancer Res. 71(17), 5635–5645 (2011)

    Article  Google Scholar 

  38. Naume, B., et al.: Detection of isolated tumor cells in bone marrow in early-stage breast carcinoma patients: comparison with preoperative clinical parameters and primary tumor characteristics. Clin. Cancer Res. 7(12), 4122–4129 (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Pure Storage, 650 Castro Street, Suite #260, Mountain View, CA, 94041, USA

    Yiqian Zhou

  2. Bioinformatics Facility, Wistar Institute, 3601 Spruce Street, Philadelphia, PA, 19104, USA

    Rehman Qureshi

  3. Biomedical Engineering, Drexel University, 3120 Market Street, Philadelphia, PA, 19104, USA

    Ahmet Sacan

Authors
  1. Yiqian Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rehman Qureshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmet Sacan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmet Sacan .

Editor information

Editors and Affiliations

  1. Georgia State University, Atlanta, Georgia, USA

    Zhipeng Cai

  2. University of Texas at Dallas, Richardson, Texas, USA

    Ovidiu Daescu

  3. Central South University, Changsha, China

    Min Li

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Zhou, Y., Qureshi, R., Sacan, A. (2017). Analysis of Paired miRNA-mRNA Microarray Expression Data Using a Stepwise Multiple Linear Regression Model. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59575-7_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59574-0

  • Online ISBN: 978-3-319-59575-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation