Figure 3. The distribution of the number of SNP occurring miRNAs. The blue triangle indicates the distribution of the number of randomly SNP occurred miRNAs in the miRNAs implicated in at least one disease. The red arrow indicates the real number of SNP occurred miRNAs in the miRNAs implicated in at least one disease. doi:10.1371/journal.pone.0003420.g003 sis[22]. Their finding provided us clues that neighboring miRNAs may be associated with the same disease. To investigate this issue, we identified miRNA clusters that contain neighboring miRNAs and then analyzed the relationship of miRNA clusters and diseases. We found that miRNAs in 46% of the diseases have at least one neighboring member, which is significantly higher than the random (P = 2.061024, Randomization Test). For example, all the 6 miRNAs implicated in hematopoietic malignancies are located in the miR-17 cluster. This result indicated that neighboring miRNAs might be regulated by common regulators at similar conditions and function together, and then whose dysfunctions would result in the same disease. According to the miRNA family and miRNA cluster analysis above, if most members in a miRNA set are associated with one disease, the other members will have a great probability to be associated with that disease too. Therefore, this finding can be used to guide us to predict novel diseases-associated miRNAs. Validation in new dataset All the above analysis was performed on the miRNA-disease association data before November 2007. In order to test the validation of the main results, here we take the miRNA-disease association data between November 2007 and June 2008 to validate the main patterns we found. We first investigated the dysfunctions of miRNAs in similar diseases and different diseases. As a result, 69% (18/26) of the disease pairs in the same class (cancer or other diseases) show similar miRNAs dysfunctions, which is higher than that (33%, 5/15) of the disease pairs between difference disease classes (P = 0.03, Fisher’s Exact Test). For example, 5 of the 6 papers revealed a down-regulation of let-7 in cancers, which is consistent with previous report. While all papers reported an up-regulation of let-7 in Alzheimer’s disease (see our HMDD database). In the new dataset, miR-195 was reported to be down regulated in human B cell lymphomas[23], which is also consistent with the previous results. We next tested the tissue specificity and disease issue. In our analysis, miR-9 has the highest expression in brain and has a high tissue specificity index (0.63), which is predicted to be associated with brain related diseases. Later research revealed that miR-9 is associated MicroRNAs and Human Disease with Alzheimer’s disease[24], which supported our prediction. MiR- 126 has the highest expression in cardiovascular tissues in our analysis and was reported has high heart specificity[25], and therefore was predicted to be associated with cardiovascular disease and validated by later research[26], in which Harris et al. found miR-126 is associated with vascular inflammation. The SNP occurring probability of miRNAs that are associated with diseases in the new data is 0.0532 (5/94), which is even less than the SNP occurring probability of miRNAs that are associated with diseases in the original data (0.0847). For the miRNA set, we found that miRNAs in 59% (13/22, similar with the original percentage 57%) of the diseases have at least one family member in that disease- associated miRNAs and miRNAs in 45% (10/22, similar with the original percentage 46%) of the diseases have at least one cluster member in that disease-associated miRNAs. These results in the new data suggest the patterns we found are valid and robust. Discussion In a conclusion, we integrated the published human miRNA disease associations and performed a comprehensive analysis to these association data. We uncovered some important patterns between miRNAs and human diseases. These findings will provide help in not only the understanding of human disease and miRNAs but also the identification of novel disease biomarkers at the miRNA level. Although the study of miRNAs and diseases is an ongoing process and the miRNA-disease association data are far from completeness, our analysis have uncovered statistically significant patterns of miRNA-disease associations. On the other hand, we also noted that some information could not be provided by our analysis. For example, several brain related diseases such as schizophrenia, Parkinson’s disease, and neurodegeneration, are not connected to each other in this study. This probably resulted from the incompleteness of the current data. As more compre- hensive data becomes available, it will do great help to similar analysis in the future. Another limitation of the current data is that some diseases are not at the same level. For example, B cell lymphoma and mantle cell lymphoma are both belong to lymphoma. Although limitations exist in the current data, the patterns uncovered here is important for understanding the association of miRNAs and various diseases. Materials and Methods Network analysis The network is visualized by Pajek (http://vlado.fmf.uni-lj.si/ pub/networks/pajek/). miRNA expression profile data We obtained the normalized gene expression profile of 345 miRNAs in 40 normal tissues that included specimens derived from brain, muscle, circulatory, respiratory, lymphoid, gastroin- testinal, urinary, reproductive, and endocrine systems[15]. The tissue specificity of a miRNA’s expression was calculated based on the tissue specificity index t proposed by Yanai et al[16]. The t of miRNA i is defined as tH ~ PnH h log2SH ði,jÞ i j~1 1{ log2SH ði,maxÞ nH {1 where nH is the number of human tissues (here nH = 40) and SH(i,max) is the highest expression value of miRNA i across the nH tissues. PLoS ONE | www.plosone.org 4 October 2008 | Volume 3 | Issue 10 | e3420