Bridging miRNAs and pathway analysis in clinical decision support: a case study in nephroblastoma
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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.
KeywordsmiRNAs Gene regulatory networks Pathway analysis Predictive models Clinical decision support Systems biology
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.
Compliance with ethical standards
Research involving human participants and/or animals
None. The models have been trained and tested using public data from GEO.
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