An Approach to Identify miRNA Associated with Cancer Altered Pathways

  • Giovanna Maria Ventola
  • Antonio Colaprico
  • Fulvio D’Angelo
  • Vittorio Colantuoni
  • Giuseppe Viglietto
  • Luigi Cerulo
  • Michele Ceccarelli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8158)


MicroRNAs play an important role in the regulation of gene expression by binding mRNA targets causing their degradation or blocking their translation. Several genes has been found to be implicated as miRNA targets in different types of malignant tumors suggesting their involvement in cancer pathogenesis. Detecting direct miRNA–targets associations is not straightforward as in principle targets expressions are not altered except when they are completely repressed by the degradation complex.

In this paper we propose an approach to identify direct miRNA–targets associations hypotheses by means of indirect association measures such as mutual information. Indirect regulons of miRNA and Transcription Factors (TFs) are compared with the Fisher’s exact test to identify potential co-regulations which may constitute potential miRNA–TF direct associations.

We apply the method on two cancer datasets, Colon and Lung, drawn from the Cancer Genome Atlas (TGCA) obtaining promising results.


miRNA reverse engineering gene regulatory networks 


  1. 1.
    Bartel, D.P.: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116, 281–297 (2004)CrossRefGoogle Scholar
  2. 2.
    Beveridge, N., Tooney, P., Carroll, A., Tran, N., Cairns, M.: Down-regulation of mir-17 family expression in response to retinoic acid induced neuronal differentiation. Cell Signal 21(12), 1837–1845 (2009)CrossRefGoogle Scholar
  3. 3.
    Coronello, C., Benos, P.: Comir: combinatorial microRNA target prediction tool. Nucleid Acid Research (2013)Google Scholar
  4. 4.
    Diosdado, B., van de Wiel, M.A., Terhaar Sive Droste, J.S., Mongera, S., Postma, C., Meijerink, W.J., Carvalho, B., Meijer, G.A.: MiR-17-92 cluster is associated with 13q gain and c-myc expression during colorectal adenoma to adenocarcinoma progression. British Journal of Cancer 101(4), 707–714 (2009)CrossRefGoogle Scholar
  5. 5.
    Dweep, H., Sticht, C., Pandey, P., Gretz, N.: miRWalk–database: prediction of possible miRNA binding sites by ”walking” the genes of three genomes. Journal of Biomedical Informatics 44(5), 839–847 (2011)CrossRefGoogle Scholar
  6. 6.
    Calin, G.A., et al.: Frequent deletions and down-regulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences of the United States of America 99(24), 15524–15529 (2002)CrossRefGoogle Scholar
  7. 7.
    Lefebvre, C., et al.: A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers. Molecular Systems Biology 6 (June 2010)Google Scholar
  8. 8.
    Genovese, G., et al.: MicroRNA regulatory network inference identifies miR-34a as a novel regulator of TGF-β signaling in glioblastoma. Cancer Discovery 13 (2012)Google Scholar
  9. 9.
    Hur, K., Toiyama, Y., Takahashi, M., Balaguer, F., Nagasaka, T., Koike, J., Hemmi, H., Koi, M., Boland, C., Goel, A.: Microrna-200c modulates epithelial-to-mesenchymal transition (emt) in human colorectal cancer metastasis. Gut (2012)Google Scholar
  10. 10.
    Earle, J.S., Luthra, R., Romans, A., Abraham, R., Ensor, J., Yao, H., Hamilton, S.R.: Association of microrna expression with microsatellite instability status in colorectal adenocarcinoma. J. Mol. Diagn. 12(4) (2010)Google Scholar
  11. 11.
    Li, J., Du, L., Yang, Y., Wang, C., Liu, H., Wang, L., Zhang, X., Li, W., Zheng, G., Dong, Z.: Mir-429 is an independent prognostic factor in colorectal cancer and exerts its anti-apoptotic function by targeting sox2. Cancer Lett. 329(1), 84–90 (2013)CrossRefGoogle Scholar
  12. 12.
    Liu, X., Zhu, W., Huang, Y., Ma, L., Zhou, S., Wang, Y., Zeng, F., Zhou, J., Zhang, Y.: High expression of serum mir-21 and tumor mir-200c associated with poor prognosis in patients with lung cancer. Med. Oncol. (2011)Google Scholar
  13. 13.
    Liu, Z.Y., Zhang, G.L., Wang, M.M., Xiong, Y.N., Cui, H.Q.: Microrna-663 targets tgfb1 and regulates lung cancer proliferation. Asian Pac. J. Cancer Prev. 12(11), 2819–2823 (2011)Google Scholar
  14. 14.
    Luo, H., Zou, J., Dong, Z., Zeng, Q., Wu, D., Liu, L.: Up-regulated mir-17 promotes cell proliferation, tumour growth and cell cycle progression by targeting the rnd3 tumour suppressor gene in colorectal carcinoma. Biochem. J. 442(2), 311–321 (2012)CrossRefGoogle Scholar
  15. 15.
    Nohata, N., Hanazawa, T., Kinoshita, T., Okamoto, Y., Seki, N.: MicroRNAs function as tumor suppressors or oncogenes: Aberrant expression of microRNAs in head and neck squamous cell carcinoma. Auris Nasus Larynx (2012)Google Scholar
  16. 16.
    Reyes-Herrera, P.H., Ficarra, E.: One Decade of Development and Evolution of MicroRNA Target Prediction Algorithms. Genomics, Proteomics & Bioinformatics 10(5), 254–263 (2012)CrossRefGoogle Scholar
  17. 17.
    Robinson, M.D., McCarthy, D.J., Smyth, G.K.: edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1), 139–140 (2010)CrossRefGoogle Scholar
  18. 18.
    Sidler, D., Renzulli, P., Schnoz, C., Berger, B., Schneider-Jakob, S., Flück, C., Inderbitzin, D., Corazza, N., Candinas, D., Brunner, T.: Colon cancer cells produce immunoregulatory glucocorticoids. Oncoimmunology 1(4), 529–530 (2012)CrossRefGoogle Scholar
  19. 19.
    Smet, R.D., Marchal, K.: Advantages and limitations of current network inference methods. Nature Reviews Microbiology 8(10), 717 (2010)Google Scholar
  20. 20.
    Sun, J., Gong, X., Purow, B., Zhao, Z.: Uncovering microRNA and transcription factor mediated regulatory networks in glioblastoma. PLoS Computational Biology (2012)Google Scholar
  21. 21.
    Sun, K., Wang, W., Zeng, J., Wu, C., Lei, S., Li, G.: Microrna-221 inhibits cdkn1c/p57 expression in human colorectal carcinoma. Acta Pharmacol Sin. 32(3), 375–384 (2011)CrossRefGoogle Scholar
  22. 22.
    Wingender, E., Dietze, P., Karas, H., Knüppel, R.: TRANSFAC: A Database on Transcription Factors and Their DNA Binding Sites. Nucleic Acids Research 24(1), 238–241 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Giovanna Maria Ventola
    • 2
  • Antonio Colaprico
    • 2
  • Fulvio D’Angelo
    • 2
  • Vittorio Colantuoni
    • 1
  • Giuseppe Viglietto
    • 3
  • Luigi Cerulo
    • 1
    • 2
  • Michele Ceccarelli
    • 1
    • 2
  1. 1.Dep. of Science and TechnologyUniversity of SannioBeneventoItaly
  2. 2.BioGeM, Institute of Genetic Research “G. Salvatore”Ariano IrpinoItaly
  3. 3.Dep. of Experimental and Clinical MedicineUniversity of “Magna Graecia”CatanzaroItaly

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