Skip to main content
Log in

Bridging miRNAs and pathway analysis in clinical decision support: a case study in nephroblastoma

  • Original Article
  • Published:
Network Modeling Analysis in Health Informatics and Bioinformatics Aims and scope Submit manuscript

Abstract

Wilms’ tumor, or nephroblastoma, is a cancer of the kidneys that typically occurs in children and rarely in adults. Around 10 % of Wilms’ tumor patients are diagnosed having a concurrent syndrome that enhances the risk of Wilms’ tumor. A screening method for early detection of Wilms’ tumor in these patients would be beneficial, since the size or stage of a tumor is related to outcome. In this paper, we introduce a miRNA pathway analysis methodology that takes into account the topology and regulation mechanisms of the gene regulatory networks and identify disrupted sub-paths in known pathways, using miRNA expressions. The methodology was applied on a miRNA expression study and a predictive model was developed, using machine-learning (decision-tree induction) approaches. The final predictive model has been integrated with the clinical decision support platform of the p-medicine EU project to provide indicative information about a patient’s phenotype in a clinical setting. Using this integrated software, a clinician is able to identify putative mechanisms that underlie and govern the Wilms’ tumor phenotype, and discriminate between diseased and healthy subjects. Initial experimental results are promising and in line with the relevant biomedical literature.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. http://www.p-medicine.eu.

  2. http://www.jessrules.com (accessed 05/03/2014).

  3. http://www.drools.org (accessed 05/03/2014).

  4. http://www.minepath.org (accessed 05/03/2015).

  5. http://mirtarbase.mbc.nctu.edu.tw/ (accessed 05/03/2015).

  6. http://www.ncbi.nlm.nih.gov/geo (accessed 05/03/2015).

References

  • Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116:281–297

    Article  Google Scholar 

  • Breuer K, Foroushani AK, Laird MR, Chen C, Sribnaia A, Lo R, Winsor GL, Hancock REW, Brinkman FSL, Lynn DJ (2012) InnateDB: systems biology of innate immunity and beyond—recent updates and continuing curation. Nucleic Acids Res. doi:10.1093/nar/gks1147

    Google Scholar 

  • Brignole C, Marimpietri D, Pastorino F, Nico B, Di Paolo D, Cioni M, Piccardi F et al (2006) Effect of bortezomib on human neuroblastoma cell growth, apoptosis and angiogenesis. J Natl Cancer Inst 98(16):1142–1157

    Article  Google Scholar 

  • Brown RE, Tan D, Taylor JS, Miller M, Prichard JW, Kott MM (2007) Morphoproteomic confirmation of constitutively activated mTOR, ERK, and NF-kappaB pathways in high risk neuro-blastoma, with cell cycle and protein analyte correlates. Ann Clin Lab Sci 37(2):141–147

    Google Scholar 

  • Bucur A, van Leeuwen J, Cirstea TC, Graf N (2013) Clinical decision support framework for validation of multi-scale models and personalization of treatment in oncology. BIBE, Chania

    Google Scholar 

  • Chen P-S, Su J-L, Hung M-C (2012) Dysregulation of microRNAs in cancer. J Biomed Sci 19(1):90

    Article  Google Scholar 

  • Cui Q, Yu Z, Purisima EO, Wang E (2006) Principles of microRNA regulation of a human cellular signaling network. Mol Syst Biol 2(1):46

    Google Scholar 

  • Frank E, Holmes G, Pfahringer B, Reutemann P, Ian H, Hall M (2009) The WEKA data mining software: an update. SIGKDD Explor 11(1):10–18

    Article  Google Scholar 

  • Garzon R, Calin GA, Croce CM (2009) MicroRNAs in cancer. Annu Rev Med 60:167–179

    Article  Google Scholar 

  • Ghildiyal M, Zamore PD (2009) Small silencing RNAs: an expanding universe. Nat Rev Genet 10(2):94–108

    Article  Google Scholar 

  • Graf N (2014) Biomarker and Wilms Tumor. Highlight Pediatr Blood Cancer 61(2):185–186

    Article  Google Scholar 

  • Graf N, van Tinteren H, Bergeron C, Pein F, van Heuvel-Eibrink M, Sandstedt B, Schenk J-P et al (2012) Characteristics and outcome of stage II and III non-anaplastic Wilms’ tumour treated according to the SIOP trial and study 93-01. Eur J Cancer 48(17):3240–3248

    Article  Google Scholar 

  • Hsu SD, Tseng Y-T, Shrestha S, Lin Y-L, Khaleel A, Chou C-H, Chu C-F et al (2014) miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. Nucleic Acids Res 42:D78–D85

    Article  Google Scholar 

  • Iorio MV, Croce CM (2012) MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review. EMBO Mol Med 4(3):143–159

    Article  Google Scholar 

  • Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T et al (2008) KEGG for linking genomes to life and the environment. Nucleic Acids Res 36(suppl 1):D480–D484

    Google Scholar 

  • Kauffman SA (1993) The origins of order: self-organization and selection in evolution. Oxford Univ, New York

    Google Scholar 

  • Keller A, Backes C, Al-Awadhi M, Gerasch A, Kuentzer J, Kohlbacher O, Kaufmann M, Lenhof HP (2008) GeneTrailExpress: a web-based pipeline for the statistical evaluation of microarray experiments. BMC Bioinform 9:552

    Article  Google Scholar 

  • Kondylakis H, Koumakis L, Genitsaridi E, Tsiknakis M, Marias K, Pravettoni G, Gorini A, Mazzocco K (2012) IEmS: A collaborative environment for patient empowerment. BIBE. Chania, Greece. 535–540

  • Koumakis L, Moustakis V, Zervakis M, Kafetzopoulos D, Potamias G (2012) Coupling regulatory networks and microarays: revealing molecular regulations of breast cancer treatment responses. In: Maglogiannis I, Plagianakos V, Vlahavas I (eds) Artificial Intelligence: Theories and Applications. Springer, Berlin, Heidelberg, pp 239–246

    Chapter  Google Scholar 

  • Koumakis L, Potamias G, Tsiknakis M, Zervakis M, Moustakis V (2015) Integrating Microarray Data and GRNs. Methods Mol Biol. doi:10.1007/7651_2015_252

  • Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schelter JM, Castle J, Bartel DP, Linsley PS, Johnson JM (2005) Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433(7027):769–773

    Article  Google Scholar 

  • Liu H (2012) MicroRNAs in breast cancer initiation and progression. Cell Mol Life Sci 69(21):3587–3599

    Article  Google Scholar 

  • Michaelis M, Fichtner I, Behrens D, Haider W, Rothweiler F, Mack A, Cinatl J, Doerr HW, Cinatl J (2006) Anti-cancer effects of bortezomib against chemoresistant neuroblastoma cell lines in vitro and in vivo. Int J Oncol 28(2):439–446

    Google Scholar 

  • Platt J (1998) Fast training of support vector machines using sequential minimal optimization. In: Schoelkopf B, Burges C, Smola A (eds) Advances in kernel methods—support vector learning. MIT Press, Cambridge

    Google Scholar 

  • Potamias G, Koumakis L, Moustakis V (2004) Gene selection via discretized gene-expression profiles and greedy feature-elimination. In: Vouros GA, Panayiotopoulos T (eds) Methods and Applications of Artificial Intelligence. Springer, Berlin, Heidelberg, pp 256–266

    Chapter  Google Scholar 

  • Pritchard CC, Cheng Heather H, Tewari Muneesh (2012) MicroRNA profiling: approaches and considerations. Nat Rev Genet 13(5):358–369

    Article  Google Scholar 

  • Rossi S, Christ-Neumann M, Rüping S, Buffa FM, Wegener D, McVie G, Coveney PV, Graf N, Delorenzi M (2011) p-Medicine: from data sharing and integration via VPH models to personalized medicine. Ecancermedicalscience 5:218

    Google Scholar 

  • Sanchez E, Toro C, Artetxe A, Graña M, Sanín C, Szczerbicki E, Carrasco E, Guijarro F (2013) Bridging challenges of clinical decision support systems with a semantic approach. A case study on breast cancer. Pattern Recognit Lett 34(14):1758–1768

    Article  Google Scholar 

  • Santo Evan E, Stroeken Peter, Sluis Peter V, Koster Jan, Versteeg Rogier, Westerhout Ellen M (2013) FOXO3a is a major target of inactivation by PI3K/AKT signaling in aggressive neuroblastoma. Cancer Res 73(7):2189–2198

    Article  Google Scholar 

  • Schmitt J, Backes C, Nourkami-Tutdibi N, Leidinger P et al (2012) Treatment-independent miRNA signature in blood of Wilms tumor patients. BMC Genomics 13(1):379

    Article  Google Scholar 

  • Sittig D, Wright A, Osheroff JA, Middleton B, Teich Jonathan M, Ash Joan S, Campbell Emily, Bates David W (2008) Grand challenges in clinical decision support. J Biomed Inform 41(2):387–392

    Article  Google Scholar 

  • Svensson Karin, Zeidman Ruth, Trollér Ulrika, Schultz Anna, Larsson Christer (2000) Protein kinase C beta1 is implicated in the regulation of neuroblastoma cell growth and proliferation. Cell Growth Differ 11(12):641–648

    Google Scholar 

  • Tibiche C, Wang E (2008) MicroRNA regulatory patterns on the human metabolic network. Open Syst Biol J 1:1–8

    Article  Google Scholar 

  • Vasudevan S (2011) Posttranscriptional upregulation by microRNAs. WIREs RNA 3(3):311–330

    Article  MathSciNet  Google Scholar 

  • Vlachos IS, Kostoulas N, Vergoulis T, Georgakilas G, Reczko M, Maragkakis M, Paraskevopoulou MD, Prionidis K, Dalamagas T, Hatzigeorgiou AG (2012) DIANA miRPath v. 2.0: investigating the combinatorial effect of microRNAs in pathways. Nucleic Acids Res 40(W1):W498–W504

    Article  Google Scholar 

  • Witt O, Hämmerling S, Stockklausner C, Schenk J, Günther P, Behnisch W, Hamad B, Ali Al Mulla N, Kulozik A (2009) 3-cis retinoic acid treatment of a patient with chemotherapy refractory nephroblastomatosis. J Pediatr Hematol Oncol 31(4):296–299

    Article  Google Scholar 

  • Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers, San Mateo

    Google Scholar 

  • Zeidman Ruth, Löfgren Bjarne, Påhlman Sven, Larsson Christer (1999) PKCε, via its regulatory domain and independently of its catalytic domain, induces neurite-like processes in neuroblastoma cells. J Cell Biol 4:713–726

    Article  Google Scholar 

  • Zhang Yuqing, Gan Boyi, Liu Debra, Paik JH (2011) FoxO family members in cancer. Cancer Biol Ther 12(4):253–259

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported from the European Union’s Seventh Framework Programme (FP7/2007-2013) for research, technological development and demonstration under Grant agreement No. 270089 and by the European Union (European Social Fund – ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: Heracleitus II Investing in knowledge society through the European Social Fund.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to L. Koumakis.

Ethics declarations

Disclosure

None.

Research involving human participants and/or animals

None. The models have been trained and tested using public data from GEO.

Informed consent

Non applicable.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Koumakis, L., Sigdel, K., Potamias, G. et al. Bridging miRNAs and pathway analysis in clinical decision support: a case study in nephroblastoma. Netw Model Anal Health Inform Bioinforma 4, 30 (2015). https://doi.org/10.1007/s13721-015-0102-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13721-015-0102-5

Keywords

Navigation