largely localized in the 39-untranslated regions (UTRs) of mRNA targets; consequently, a given miRNA can potentially impact hundreds of genes within and across diverse signaling pathways [3,4,5,6,7]. MiRNAs, along with gene copy number alterations and methyl- ation of gene promoter regions, globally influence gene expression, which ultimately determines cellular behavior. The Cancer Genome Atlas (TCGA) project is a large-scale collaborative effort which seeks to comprehensively catalogue the molecular aberrations in various cancers. While the recent initial report from TCGA on 489 high- grade serous ovarian adenocarcinomas (487 of which had corre- sponding miRNA data) [8] presented a broad molecular picture of the disease, much in terms of miRNAs remains to be elucidated. Here, we comprehensively survey the miRNAs within the TCGA ovarian dataset, making use of the various molecular profile data types, miRNA and mRNA in particular, that have all been generated for the same set of tumors. Previous miRNA expression profiling studies of ovarian cancer have defined differentially expressed miRNAs in cancer relative to the corresponding normal control [9,10,11,12,13,14], though in this present study, such a large dataset (n = 487 patients) allows us to more fully explore the diversity of miRNAs within a single ovarian cancer subtype. Here, we present a number of findings on miRNAs in ovarian cancer, from various integration-based analyses. These analyses revolve around the basic question of whether the general rules of miRNA behavior, as we currently understand them, can be supported by corroborating patterns within human cancers. Our study serves to reinforce our current notions of basic miRNA biology, to demonstrate how current in silico miRNA-gene targeting predictions may be refined through integrative analysis, and to demonstrate the rich resource of TCGA in identifying miRNA candidates for functional targeting in cancer. Our study also provides second-level data mining results for molecular biologists to more deeply explore specific miRNA- associated pathways in ovarian cancer. Results MiRNAs are influenced by both copy number alteration and genomic location We examined the TCGA ovarian cancer datasets, representing 487 tumors profiled for miRNA expression, for patterns of correlation between the miRNAs and other molecular features, to see whether the overall trends observed would fit our initial expectations. To begin with, we considered that miRNAs with expression levels frequently altered by changes in DNA copy number may reveal a subset of miRNAs under clonal selection in the tumors; such miRNAs would be of potential interest as candidate oncomiRs or tumor suppressive miRs. We therefore systematically analyzed miRNAs for both loss and gain of DNA copy number associated with a concordant change in mature miRNA expression level (Figure 1A, Dataset S1). This analysis revealed several miRNAs in focally amplified and deleted genomic regions. In particular, let-7b was the most frequently deleted miRNA having both recurrent hemizygous genomic loss (86% of samples) and homozygous deletion (7.2%). Another deleted miRNA, miR-31, was recently found by our group to suppress ovarian cancer cell proliferation [10]. Four members of the miR- 30 family were among the most frequently amplified miRNAs. Interestingly, these members were encoded at two different focally amplified loci (8q24 and 1p34) and all four miRNAs showed strong concordant change in mature miRNA expression. In our data, we also found miRNAs to be frequently coexpressed with neighboring miRNAs as anticipated. Previously, when examining miRNA expression profiles in a small dataset of 24 miRNA-Gene Integration in Ovarian Cancer normal human tissues, Baskerville and Bartel found evidence that proximal pairs of miRNAs are generally coexpressed (suggesting that they are processed from polycistronic primary transcripts), and that intronic miRNAs are usually coexpressed with their host gene mRNA (suggesting that they both derive from a common transcript) [15]. To extend these preliminary observations to ovarian cancer (thereby reinforcing current notions of miRNA biology as well as the integrity of our TCGA data), we made pairwise comparisons for each chromosome between the expression profiles of all miRNAs oriented in the same direction, calculating for each pair a correlation coefficient; the results showed that most miRNA genes within 50– 100 kb of each other had highly correlated expression patterns (Figure 1B). Notably, at distances beyond 100 kb (exceeding the length of most human genes), the correlation between pairs dropped dramatically to zero. While DNA copy number alterations (CNA) undoubtedly influence gene and miRNA expression in cancer [10,16], pairwise correlations in copy number levels between proximal miRNAs showed a very different pattern from the pairwise expression correlations; high proximal correlations for copy number extended for .1 Mb in length, with no dramatic drop (Figure S1A). Approximately 177 of the 558 mature human miRNAs profiled are located in the genome within the introns of host genes, and we found miRNAs to be frequently coexpressed with these host genes in our data. For each of 188 miRNA-host gene pairs (each comprised of a miRNA located within the boundaries of a known gene, same orientation, where some mature miRNAs have multiple genomic locations), we computed the correlation between miRNA and host gene expression. MiRNA-host gene pairs tended to be strongly correlated with each other and, with 52% of the miRNA-host gene pairs with available data showing significant positive correlation (P,0.01), in agreement with previous studies [17,18] (Figure 1C). As expected, miRNA expression was also correlated with host gene copy number, though the correlations were not as strong as for gene expression (Figure S1B). Diversity of miRNA and gene expression patterns suggestive of ovarian tumor subtypes Previously, unsupervised clustering of miRNA expression data had suggested three general groupings or subtypes–with designations C1, C2, and C3–of high-grade serous ovarian tumors. The C1 subtype had been associated with worse patient survival as compared to the other two subtypes; in addition, the C1 subtype overlapped somewhat with the gene expression-based ‘‘proliferative’’ subtype, and the C2/C3 subtypes overlapped more with the gene-based ‘‘mesenchymal’’ subtype [8]. The average ‘‘silhouette’’ widths of the miRNA-based clusters had indicated them to be somewhat weakly defined with substantial within-group heterogeneity, while still reflecting patterns of biological diversity [8]. As described below, here we sought to further characterize these miRNA-based clusters, using our mRNA and miRNA expression data. Given the role of miRNAs in repressing gene expression, we might anticipate that miRNAs and their mRNA targets in general would appear anti-correlated in expression in human tumors. A number of computational algorithms—the most well-known of these being miRanda [4] and TargetScan [7]—have been developed to predict the targeting of a given gene transcript by a specific miRNA (based on both sequence alignment of miRNA to gene 39-UTR and on species conservation). By integration of both miRNA and gene expression patterns using previously-used approaches [10], we could define putative miRNA:mRNA functional pairs underlying the miRNA-based subtypes. Using the set of miRNAs and genes differing significantly between the groups, miRNA:mRNA pairs were defined by both predicted targeting association and anti-correlation in expression patterns PLoS ONE | www.plosone.org 2 March 2012 | Volume 7 | Issue 3 | e34546