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

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...

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Bibliographic Details
Published in:Network modeling and analysis in health informatics and bioinformatics (Wien) 2015-12, Vol.4 (1), p.30, Article 30
Main Authors: Koumakis, L., Sigdel, K., Potamias, G., Sfakianakis, S., van Leeuwen, J., Zacharioudakis, G., Moustakis, V., Zervakis, M., Bucur, A., Marias, K., Graf, N., Tsiknakis, M.
Format: Article
Language:English
Subjects:
Applications of Graph Theory and Complex Networks
Bioinformatics
Cancer
Clinical decision making
Clinical medicine
Computational Biology/Bioinformatics
Computer Science
Data mining
Decision analysis
Decision support systems
Decision trees
Genes
Genomics
Health Informatics
Knowledge discovery
Machine learning
MicroRNAs
miRNA
Original Article
Patient satisfaction
Phenotypes
Precision medicine
Prediction models
Software
Topology
Tumors
Citations: Items that this one cites
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Summary: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.
ISSN:2192-6662
2192-6670
DOI:10.1007/s13721-015-0102-5