An Analysis of Human MicroRNA and Disease Associations

Abstract

It has been reported that increasingly microRNAs are associated with diseases. However, the patterns among the microRNA-disease associations remain largely unclear. In this study, in order to dissect the patterns of microRNA-disease associations, we performed a comprehensive analysis to the human microRNA-disease association data, which is manually collected from publications. We built a human microRNA associated disease network. Interestingly, microRNAs tend to show similar or different dysfunctional evidences for the similar or different disease clusters, respectively. A negative correlation between the tissue-specificity of a microRNA and the number of diseases it associated was uncovered. Furthermore, we observed an association between microRNA conservation and disease. Finally, we uncovered that microRNAs associated with the same disease tend to emerge as predefined microRNA groups. These findings can not only provide help in understanding the associations between microRNAs and human diseases but also suggest a new way to identify novel disease-associated microRNAs.

MiRNA Synergistic Network Construction and Enrichment Analysis for Common Target Genes in Small-cell Lung Cancer

“…Moreover, these three miRNAs are all proved to be related to lung cancer (Johnson et al, 2007;Agirre et al, 2008). Our results were corresponding with the former researches, which indicated that the deregulation of miRNAs belonging to the same family may lead to the similar abnormal phonotype (Lu et al, 2008). What's more, these three miRNAs were predicted to be co-expressed according to our synergistic network.…”

Ontology-based disease similarity network for disease gene prediction

“…Indeed, a recent RWRH-based method has used a semantic similarity network of genes instead of the protein interaction network [34] and shown to outperform the original one [32]. We also note that a disease similarity network can be constructed based on shared disease gene [30], shared pathways [35], shared miRNA [36], shared protein complex [37], shared disease ontology [38] and disease comorbidity [39]. Similarly to RWR, RWRH algorithm has been successfully applied to other problems such as prediction of novel drugtarget interactions [40] as well as novel disease-associated miRNAs [41] and long non-coding RNAs [42].…”

Identification of aberrant circulating miRNAs in Parkinson's disease plasma samples

“…miRNAs mostly repress translation or induce mRNA degradation of the target genes by complementary interactions (Bartel, 2009; Griffiths‐Jones, Grocock, van Dongen, Bateman, & Enright, 2006). Around 1%–4% genes in the human genome encode for miRNAs and about one‐third of mRNAs are regulated by miRNAs (Lu et al., 2008). It has been well demonstrated that miRNAs play critical roles in many biological processes including cell fate determination, embryonic development, cell proliferation, differentiation, and apoptosis (Esquela‐Kerscher & Slack, 2006).…”