An analysis of human microRNA and disease associations

• Published in: PloS one Vol. 3; no. 10; p. e3420
• Main Authors: Lu, Ming, Zhang, Qipeng, Deng, Min, Miao, Jing, Guo, Yanhong, Gao, Wei, Cui, Qinghua
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 15.10.2008; Public Library of Science (PLoS)
• Subjects: Acids; Alzheimer's disease; Alzheimers disease; Analysis; Bioinformatics; Cancer; Cardiology; Cardiovascular disease; Cell adhesion & migration; Cell growth; Computational Biology; Computational Biology/Systems Biology; Computational Biology/Transcriptional Regulation; Conservation; Databases, Nucleic Acid; Diseases; Education; Epidemiology; Gene expression; Genetic Predisposition to Disease; Genomes; Genomics; Health informatics; Humans; Laboratories; Medical Informatics; Medical research; MicroRNA; MicroRNAs; MicroRNAs - analysis; MicroRNAs - genetics; miRNA; Mutation; Polymorphism, Single Nucleotide; Ribonucleic acid; RNA; Science; Tissue Distribution; Tumorigenesis; Beijing China; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0003420

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.