An Analysis of Human MicroRNA and Disease Associations

By Ming Lu, Qipeng Zhang, Min Deng, Jing Miao, Yan-hong Guo, Wei Gao, Qinghua Cui. PLoS ONE, 2008

919 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2bbd959d9b31c29f1e801b55fabdc7a65c4a3121

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Xing Chen
2. Yvonne Tay
3. Jia Qu
4. P. Pandolfi
5. Guiying Yan
6. Lev M. Kats
7. L. Salmena
8. L. Poliseno

In Molecular Biology

1. Xing Chen
2. P. Pandolfi
3. Yvonne Tay
4. Jia Qu
5. Guiying Yan
6. L. Salmena
7. Lei Wang
8. Na-Na Guan

Citations in the corpus

• Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “Type of related data (types of network nodes)a Name and additional description, website Referencesb • Disease • Disease related genes Human disease network (Cytoscape plug-in DisGeNET: http://ibi.imim.es/DisGeNET/ DisGeNETweb.html) Goh et al., 2007; Feldman et al., 2008; Bauer-Mehren et al., 2010; S”
• Semi-supervised learning for potential human microRNA-disease associations inference (2014, methodology): “Lu, et al. 30 integrated and analyzed these disease-miRNA associations to obtain some important patterns between human diseases and miRNAs, which not only benefited the understanding of human diseases at miRNA level, but also laid the solid theoretical fundament for the identification of novel disea”
• Predicting miRNA‐disease association based on inductive matrix completion (2018, methodology): “, 2007; Lu et al., 2008). Thanks to the excellent work of Wang et al. (2010), we can download the miRNA functional similarity data from http://www.”
• Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors (2013, methodology): “It is observed that miRNAs with similar functions are often associated with similar diseases and vice versa [8,20,28,29].”
• A ceRNA hypothesis: the Rosetta stone of a hidden RNA language? (2011, background): “In fact, microRNAs have been implicated in numerous diseases (http://cmbi.bjmu. edu.cn/hmdd), including cancer (Calin et al., 2002; Lu et al., 2008).”