Skip to main content

Advertisement

Log in

Identifying significant microRNA–mRNA pairs associated with breast cancer subtypes

  • Short Communication
  • Published:
Molecular Biology Reports Aims and scope Submit manuscript

Abstract

MicroRNAs (miRNAs) are small non-coding RNAs that help in post-transcriptional gene silencing. These endogenous RNAs develop a post-transcriptional gene-regulatory network by binding to complementary sequences of target mRNAs and essentially degrade them. Cancer is a class of diseases that is caused by the uncontrolled cell growth, thereby resulting into a gradual degradation of cell structure. Earlier researches have shown that miRNAs have significant biological involvement in cancer. Prolonged research in this genre has led to the identification of the functions of numerous miRNAs in cancer development. Studying the differential expression profiles of miRNAs and mRNAs together could help us in recognizing the significant miRNA–mRNA pairs from cancer samples. In this paper, we have analyzed the simultaneous over-expression of miRNAs and under-expression of mRNAs and vice versa to establish their association with cancer. This study focuses on breast tumor samples and the miRNA–mRNA target pairs that have a visible signature in such breast tumor samples. We have been able to identify the differentially expressed miRNAs and mRNAs, and further established relations between them to extract the miRNA–mRNA pairs that might be significant in the breast cancer types. This gives us the clue about the potential biomarkers for the breast cancer subtypes that can further help in understanding the progression of each of the subtypes separately. This might be helpful for the joint miRNA–mRNA biomarker identification.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136(2):215–233

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Gordon GJ et al (2002) Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Res 62(17):4963–4967

    CAS  PubMed  Google Scholar 

  3. Iorio MV et al (2005) MicroRNA gene expression deregulation in human breast cancer. Cancer Res 65(16):7065–7070

    Article  CAS  PubMed  Google Scholar 

  4. Michael MZ, Connor O`, van Holst Pellekaan NG, Young GP, James RJ (2003) Reduced accumulation of specific microRNAs in colorectal neoplasia. Mol Cancer Res 1:882–891

    CAS  PubMed  Google Scholar 

  5. Metzler M, Wilda M, Busch K, Viehmann S, Borkhardt A (2004) High expression of precursor microRNA-155/BIC RNA in children with Burkitt lymphoma. Genes Chromosomes Cancer 39:167–169

    Article  CAS  PubMed  Google Scholar 

  6. Eis PS, Tam W, Sun L et al (2005) Accumulation of miR-155 and BIC RNA in human B cell lymphomas. Proc Natl Academy Sci USA 102:3627–3632

    Article  CAS  Google Scholar 

  7. Bhattacharyya M, Nath J, Bandyopadhyay S (2015) MicroRNA signatures highlight new breast cancer Subtypes. Gene 556(2):192–198

    Article  CAS  PubMed  Google Scholar 

  8. Suzuki HI et al (2013) Widespread inference of weighted microRNA-mediated gene regulation in cancer transcriptome analysis. Nucleic Acids Res 41(5):e62–e62

    Article  CAS  PubMed  Google Scholar 

  9. Dvinge H et al (2013) The shaping and functional consequences of the microRNA landscape in breast cancer. Nature 497(7449):378–382

    Article  CAS  PubMed  Google Scholar 

  10. Rishabh K, Peek GW, Hardy TM, Tollefsbol TO (2013) MicroRNAs: an emerging science in cancer epigenetics. J Clin Bioinform 3:6

    Article  Google Scholar 

  11. Kim VN, Han J, Siomi MC (2009) Review Biogenesis of small RNAs in animals. Nat Rev Mol Cell Biol 10(2):126–139

    Article  CAS  PubMed  Google Scholar 

  12. Xi Y et al (2006) Differentially regulated micro-RNAs and actively translated messenger RNA transcripts by tumor suppressor p53 in colon cancer. Clin Cancer Res 12(7):2014–2024

    Article  CAS  PubMed  Google Scholar 

  13. Fabbri M, Garzon R, Cimmino A, Liu Z, Zanesi N, Callegari E, Liu S, Alder H, Costinean S, Fernandez-Cymering C, Volinia S, Guler G, Morrison CD, Chan KK, Marcucci G, Calin GA, Huebner K, Croce CM (2007) MicroRNA-29 family reverts aberrant methylation in lung cancer by targeting DNA methyltransferases 3A and 3B. Proc Natl Acad Sci USA 104(40):15805–15810

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Lu Y et al (2012) Oncogenic function and early detection potential of miRNA-10b in oral cancer as identified by microRNA profiling. Cancer Prev Res 5(4):665–674

    Article  CAS  Google Scholar 

  15. Kang K et al (2012) Identification of circulating miRNA biomarkers based on global quantitative real-time PCR profiling. J Anim Sci Biotechnol 3(1):4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Lu J et al (2005) MicroRNA expression profiles classify human cancers. Nature 435(7043):834–838

    Article  CAS  PubMed  Google Scholar 

  17. Yan Z et al (2012) Integrative analysis of gene and miRNA expression profiles with transcription factor–miRNA feed-forward loops identifies regulators in human cancers. Nucleic Acids Res 40(17):e135–e135

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Yu F, Yao H, Zhu P, Zhang X, Pan Q, Gong C, Huang Y, Hu X, Su F, Lieberman J, Song E (2007) let-7 regulates self renewal and tumorigenicity of breast cancer cells. Cell 131:1109–1123

    Article  CAS  PubMed  Google Scholar 

  19. Li D, Xia H, Li Z, Hua L, Li L (2015) Identification of novel breast cancer subtype-specific biomarkers by integrating genomics analysis of dna copy number aberrations and miRNA-mRNA dual expression profiling. BioMed Res Int 2015:746970

    PubMed  PubMed Central  Google Scholar 

  20. Vergoulis T, Vlachos I, Alexiou P, Georgakilas G, Maragkakis M, Reczko M, Gerangelos S, Koziris N, Dalamagas T, Hatzigeorgiou AG (2012) TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res 40(D1):D222–D229

    Article  CAS  PubMed  Google Scholar 

  21. Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4(1):44–57

    Article  CAS  Google Scholar 

  22. Warde-Farley D et al (2010) The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 38(Suppl 2):W214–W220

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Vaughan RA, Gannon NP, Garcia-Smith R, Licon-Munoz Y, Barberena MA, Bisoffi M, Trujillo KA (2014) β-alanine suppresses malignant breast epithelial cell aggressiveness through alterations in metabolism and cellular acidity in vitro. Mol Cancer 13:14

    Article  PubMed  PubMed Central  Google Scholar 

  24. Chatterjee P, Bhattacharyya M, Bandyopadhyay S, Roy D (2014) Studying the system-level involvement of microRNAs in Parkinson’s disease. PLoS One 9(4):e93751

    Article  PubMed  PubMed Central  Google Scholar 

  25. Mukhopadhyay A, Maulik U (2013) An SVM-wrapped multiobjective evolutionary feature selection approach for identifying cancer-microRNA markers. IEEE Trans Nanobiosci 12(4):275–281

    Article  Google Scholar 

  26. The European Genome-phenome Archive. http://www.ebi.ac.uk/ega

  27. Storey JD (2002) A direct approach to false discovery rates. J Roy Stat Soc B 64(3):479–498

    Article  Google Scholar 

  28. Glaab E, Baudot A, Krasnogor N, Schneider R, Valencia A (2012) EnrichNet: network-based gene set enrichment analysis. Bioinformatics 28(18):i451

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

The work of Malay Bhattacharyya is supported by the Visvesvaraya Young Faculty Research Fellowship 2015–2016 of DeitY, Government of India.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sanghamitra Bandyopadhyay.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (XLS 45 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bhattacharyya, M., Nath, J. & Bandyopadhyay, S. Identifying significant microRNA–mRNA pairs associated with breast cancer subtypes. Mol Biol Rep 43, 591–599 (2016). https://doi.org/10.1007/s11033-016-4021-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11033-016-4021-z

Keywords

Navigation