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

• 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
• ISSN: 2192-6662; 2192-6670
• DOI: 10.1007/s13721-015-0102-5

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.