Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes

Background MicroRNAs (miRNAs) are important post-transcriptional regulators that have been demonstrated to play an important role in human diseases. Elucidating the associations between miRNAs and diseases at the systematic level will deepen our understanding of the molecular mechanisms of diseases....

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Vydané v:BMC systems biology Ročník 7; číslo 1; s. 101
Hlavní autori: Shi, Hongbo, Xu, Juan, Zhang, Guangde, Xu, Liangde, Li, Chunquan, Wang, Li, Zhao, Zheng, Jiang, Wei, Guo, Zheng, Li, Xia
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London BioMed Central 08.10.2013
BioMed Central Ltd
Predmet:
Algorithms
Analysis
Bioinformatics
Biomedical and Life Sciences
Cancer
Cellular and Medical Topics
Computational Biology - methods
Computational Biology/Bioinformatics
Disease
Disease - genetics
Disease susceptibility
Gene expression
Genetic regulation
Humans
Life Sciences
Medical research
Medicine, Experimental
Methods
MicroRNA
MicroRNAs - genetics
Nervous system diseases
pharmacology and medicine
Physiological
Protein Interaction Mapping - methods
Protein-protein interactions
Proteins
Regulation
Research Article
Simulation and Modeling
Stochastic Processes
Studies
Systems Biology
Systems physiology
Random walk analysis
Disease genes
MiRNA-disease network
MiRNA
ISSN:1752-0509, 1752-0509
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Shrnutí:Background MicroRNAs (miRNAs) are important post-transcriptional regulators that have been demonstrated to play an important role in human diseases. Elucidating the associations between miRNAs and diseases at the systematic level will deepen our understanding of the molecular mechanisms of diseases. However, miRNA-disease associations identified by previous computational methods are far from completeness and more effort is needed. Results We developed a computational framework to identify miRNA-disease associations by performing random walk analysis, and focused on the functional link between miRNA targets and disease genes in protein-protein interaction (PPI) networks. Furthermore, a bipartite miRNA-disease network was constructed, from which several miRNA-disease co-regulated modules were identified by hierarchical clustering analysis. Our approach achieved satisfactory performance in identifying known cancer-related miRNAs for nine human cancers with an area under the ROC curve (AUC) ranging from 71.3% to 91.3%. By systematically analyzing the global properties of the miRNA-disease network, we found that only a small number of miRNAs regulated genes involved in various diseases, genes associated with neurological diseases were preferentially regulated by miRNAs and some immunological diseases were associated with several specific miRNAs. We also observed that most diseases in the same co-regulated module tended to belong to the same disease category, indicating that these diseases might share similar miRNA regulatory mechanisms. Conclusions In this study, we present a computational framework to identify miRNA-disease associations, and further construct a bipartite miRNA-disease network for systematically analyzing the global properties of miRNA regulation of disease genes. Our findings provide a broad perspective on the relationships between miRNAs and diseases and could potentially aid future research efforts concerning miRNA involvement in disease pathogenesis.
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ISSN:1752-0509
1752-0509
DOI:10.1186/1752-0509-7-101