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Identifying relevant group of miRNAs in cancer using fuzzy mutual information

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Abstract

MicroRNAs (miRNAs) act as a major biomarker of cancer. All miRNAs in human body are not equally important for cancer identification. We propose a methodology, called FMIMS, which automatically selects the most relevant miRNAs for a particular type of cancer. In FMIMS, miRNAs are initially grouped by using a SVM-based algorithm; then the group with highest relevance is determined and the miRNAs in that group are finally ranked for selection according to their redundancy. Fuzzy mutual information is used in computing the relevance of a group and the redundancy of miRNAs within it. Superiority of the most relevant group to all others, in deciding normal or cancer, is demonstrated on breast, renal, colorectal, lung, melanoma and prostate data. The merit of FMIMS as compared to several existing methods is established. While 12 out of 15 selected miRNAs by FMIMS corroborate with those of biological investigations, three of them viz., “hsa-miR-519,” “hsa-miR-431” and “hsa-miR-320c” are possible novel predictions for renal cancer, lung cancer and melanoma, respectively. The selected miRNAs are found to be involved in disease-specific pathways by targeting various genes. The method is also able to detect the responsible miRNAs even at the primary stage of cancer. The related code is available at http://www.jayanta.droppages.com/FMIMS.html.

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References

  1. Arndt GM et al (2009) Characterization of global microRNA expression reveals oncogenic potential of mir-145 in metastatic colorectal cancer. BMC Cancer 9:374–390

    Article  PubMed  PubMed Central  Google Scholar 

  2. Blenkiron C et al (2007) MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype. Genome Biol 8:R214.1–R214.16

    Article  Google Scholar 

  3. Calin GA et al (2002) Frequent deletions and down-regulation of micro-RNA genes mir15 and mir16 at 13q14 in chronic lymphocytic leukemia. Proc Natl Acad Sci 99:15524–15529

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Chen HY et al (2012) miR-103/107 promote metastasis of colorectal cancer by targeting the metastasis suppressors DAPK and KLF4.  J Cancer Res 72:3631–3641

    Article  CAS  Google Scholar 

  5. Cheng CW et al (2012) MicroRNA-30a inhibits cell migration and invasion by downregulating vimentin expression and is a potential prognostic marker in breast cancer. Breast Cancer Res Treat 134:1081–1093

    Article  CAS  PubMed  Google Scholar 

  6. Feng J et al (2013) Screening biomarkers of prostate cancer by integrating microRNA and mRNA microarrays. Genet Test Mol Biomark 17:807–813

    Article  CAS  Google Scholar 

  7. Greenberg E et al (2011) Regulation of cancer aggressive features in melanoma cells by microRNAs. Plos One 6:e18936

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Guyon I et al (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46:389–422

    Article  Google Scholar 

  9. He L et al (2013) MicroRNA-181b expression in prostate cancer tissues and its influence on the biological behavior of the prostate cancer cell line pc-3. Genet Mol Res 12:1012–1021

    Article  CAS  PubMed  Google Scholar 

  10. Jung M et al (2009) MicroRNA profiling of clear cell renal cell cancer identifies a robust signature to define renal malignancy. J Cell Mol Med 13:3918–3928

    Article  PubMed  PubMed Central  Google Scholar 

  11. Keller A et al (2009) miRNAs in lung cancer-studying complex fingerprints in patient’s blood cells by microarray experiments. BMC Cancer 9:353–362

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kusy M, Obrzut B, Kluska J (2013) Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients. Med Biol Eng Comput 51:1357–1365

    Article  PubMed  PubMed Central  Google Scholar 

  13. Laganá A et al (2009) miro: a miRNA knowledge base. Database Oxf J 2009:1–7

    Google Scholar 

  14. Leidinger P et al (2010) High-throughput miRNA profiling of human melanoma blood samples. BMC Cancer 10:262–272

    Article  PubMed  PubMed Central  Google Scholar 

  15. Li J-H et al (2014) starbase v2.0: decoding mirna-cerna, mirna-ncrna and proteinrna interaction networks from large-scale clip-seq data. Nucleic Acids Res 14:D92–D97

    Article  Google Scholar 

  16. Lopez F et al (2012) Biomedical application of fuzzy association rules for identifying breast cancer biomarkers. Med Biol Eng Comput 50:981–990

    Article  CAS  PubMed  Google Scholar 

  17. Lund AH (2010) mir-10 in development and cancer. Cell Death Differ 17:209–214

    Article  CAS  PubMed  Google Scholar 

  18. Maji P, Pal SK (2010) Fuzzy-rough sets for information measures and selection of relevant genes from microarray data. IEEE Trans Syst Man Cybern B Cybern 40:741–752

    Article  PubMed  Google Scholar 

  19. Mueller DW, Rehli M, Bosserhoff AK (2009) miRNA expression profiling in melanocytes and melanoma cell lines reveals miRNAs associated with formation and progression of malignant melanoma. J Investig Dermatol 129:1740–1751

    Article  CAS  PubMed  Google Scholar 

  20. Mundra PA, Rajapakse JC (2010) SVM-RFE with MRMR filter for gene selection. IEEE Trans Nanobiosci 9:31–37

    Article  Google Scholar 

  21. Navon R et al (2009) Novel rank-based statistical methods reveal microRNAs with differential expression in multiple cancer types. PLoS One 4:e8003

    Article  PubMed  PubMed Central  Google Scholar 

  22. Nishida N et al (2012) Microarray analysis of colorectal cancer stromal tissue reveals upregulation of two oncogenic miRNA clusters.  Clin Cancer Res 18:3054–3070

    Article  CAS  PubMed  Google Scholar 

  23. Ouzounova M et al (2013) MicroRNA mir-30 family regulates non-attachment growth of breast cancer cells. BMC Genomics 14:139

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Pal JK, Ray SS, Pal SK (2013) A weighted threshold for detection of cancerous miRNA expressions. Fundam Inf 127:289–305

    Google Scholar 

  25. Papadopoulos GL et al (2009) Diana-mirpath: integrating human and mouse micrornas in pathways. Bioinformatics 15:1991–1993

    Article  Google Scholar 

  26. Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance and min-redundancy, IEEE Trans. Pattern Anal Mach Intell 27:1226–1238

    Article  Google Scholar 

  27. Ray SS, Pal JK, Pal SK (2013) Computational approaches for identifying cancer miRNA expressions. Gene Expr 15:243–253

    Article  Google Scholar 

  28. Ray SS, Maiti S (2015) Noncoding RNAs and their annotation using metagenomics algorithms. Wiley Interdiscip Rev Data Min Knowl Discov 5:1–20

    Article  Google Scholar 

  29. Rui W et al (2011) Identification of microRNA profiles in docetaxel-resistant human non-small cell lung carcinoma cells (spc-a1). J Cell Mol Med 14:206–214

    Article  Google Scholar 

  30. Salim M et al (2013) Measurement of bioelectric and acoustic profile of breast tissue using hybrid magnetoacoustic method for cancer detection. Med Biol Eng Comput 51:459–466

    Article  PubMed  Google Scholar 

  31. Schaefer A et al (2010) Diagnostic and prognostic implications of microRNA profiling in prostate carcinoma. Int J Cancer 126:1166–1176

    CAS  PubMed  Google Scholar 

  32. Su D, Ma R, Zhu L (2011) Numerical study of nanofluid infusion in deformable tissues for hyperthermia cancer treatments. Med Biol Eng Comput 49:1233–1240

    Article  PubMed  Google Scholar 

  33. Vlachos IS et al (2012) Diana mirpath v.2.0: investigating the combinatorial effect of micrornas in pathways. Nucleic Acids Res (Web server issue), pp W498–W504

  34. Wang Z et al (2007) Unravelling the world of cis-regulatory elements. Med Biol Eng Comput 45:709–718

    Article  PubMed  Google Scholar 

  35. Wulfken LM et al (2011) MicroRNAs in renal cell carcinoma: diagnostic implications of serum mir-1233 levels. Cell Death Differ 6:e25787

    CAS  Google Scholar 

  36. Zehavi L et al (2012) Silencing of a large microRNA cluster on human chromosome 14q32 in melanoma: biological effects of mir-376a and mir-376c on insulin growth factor 1 receptor. Mol Cancer 11:44–58

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Zhang N et al (2013) MicroRNA-30a suppresses breast tumor growth and metastasis by targeting metadherin. Oncogenet 3119–3128

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Acknowledgments

S. K. Pal acknowledges the J. C. Bose fellowship of the Government of India and the INAE Chair Professorship.

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Authors and Affiliations

  1. Center for Soft Computing Research, Indian Statistical Institute, Kolkata, India

    Jayanta Kumar Pal

  2. Center for Soft Computing Research, Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

    Shubhra Sankar Ray & Sankar K. Pal

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  1. Jayanta Kumar Pal
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  2. Shubhra Sankar Ray
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  3. Sankar K. Pal
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Correspondence to Jayanta Kumar Pal.

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Pal, J.K., Ray, S.S. & Pal, S.K. Identifying relevant group of miRNAs in cancer using fuzzy mutual information. Med Biol Eng Comput 54, 701–710 (2016). https://doi.org/10.1007/s11517-015-1360-1

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  • DOI: https://doi.org/10.1007/s11517-015-1360-1

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