miRNA-Gene Integration in Ovarian Cancer Figure 3. MiRNAs correlated with patient survival in ovarian cancer. Using a training dataset of TCGA miRNA expression profiles, a prognostic miRNA signature was defined (left panel) and then applied to a test dataset (right panel), each tumor being assigned a score measuring how well the tumor’s expression patterns reflected those of the signature pattern. Kaplan-Meier analysis (log-rank tests) compares time to death for ovarian cancer patients showing higher risk (prognostic score.0) versus lower risk (prognostic score,0). Univariate Cox test treats the prognostic score as a continuous variable. (miR-923 was recently determined to be an rRNA fragment, according to miRBase.). doi:10.1371/journal.pone.0034546.g003 miRNA:mRNA targeting relationships. To this end, we computed all possible miRNA:mRNA correlations across the 487 TCGA ovarian tumors, for the top expressed 191 miRNAs and 8547 genes. We then sorted the 19168547 miRNA:mRNA pairs by low to high correlation, and found that among the most anti-correlated pairs, there was high enrichment for predicted miRNA:mRNA targeting interactions by miRanda algorithm (Figure 4A), where no such enrichment was observed for the positively correlated miRNAs:mRNAs. (This trend was observed when considering all other miRNAs and genes in addition to those most highly expressed, Figure S6.) In addition to validating the public target prediction databases as being enriched for true positives, this finding indicated that thousands of miRNA:mRNA targeting interactions are active in ovarian cancer and influence tumor gene expression heterogeneity. The impact of CNA on expression level can vary greatly between genes, conceivably introducing bias when evaluating association of miRNA and gene expression levels. Therefore, in addition to a direct Pearson’s correlation between miRNA and mRNA, we applied a simple linear regression model to account for ‘noise’ due to CNA, evaluating the association between expression levels of a miRNA and mRNA, when CNA status of the gene is held fixed. Interestingly, the Pearson’s model and the regression model of miRNA:mRNA correlations both gave very similar overall results in terms of predicted target enrichment (Figure 4A), with the regression model’s negatively correlated pairs showing slightly greater target enrichment (Figure S7). While, in general, CNA did not represent a major confounding factor, the regression model could identify individual miRNA:mRNA correlations which were missed by the Pearson’s model, including miR-29a:HARS2 (Figure S8). As another way to globally represent miRNA:mRNA interac- tions in ovarian cancer, for all miRNA:mRNA pairs with the strongest negative correlation (regression coefficient ,27.0, based on the linear model), we clustered the matrix of correlation coefficients (Figure 4B, consisting of 1760 genes and 35 miRNAs, matrix data table available as Dataset S4), thereby grouping miRNAs when they are negatively correlated with same genes and vice versa. We then cut the gene dendrogram to extract 6 gene clusters (based on what appeared to be natural separations within the cluster tree), each of which was found to be uniquely enriched for different gene classes (Document S2), including a cluster with Wnt and Hedgehog pathway gene members, a cluster with cell adhesion genes, two clusters with immune response genes, and a cluster of cell cycle-related genes. For several individual miRNAs, the genes anti-correlated in expression were significantly enriched for in silico predicted targets (Figure 4B and Figure S9 and Figure S10). Genes anti-correlated with miRNAs are enriched for miRNA seeds predominantly in the 39-UTRs Popular algorithms for miRNA targeting prediction, such as miRanda or TargetScan, rely on basic assumptions, including targeting within the gene 39-UTR. While miRNAs are understood to typically bind 39-UTRs, there have been a number of studies showing target sites in coding regions as being effective [24,25], and others suggesting alternative 59-UTR targeting [26,27]. In order to determine the overall trends as indicated by our data, in terms of where miRNAs tend to bind, we examined miR- NA:mRNA pairs anti-correlated in expression for the presence of miRNA seed sequences (7mers) in either the 39-UTR, 59-UTR, or coding sequence regions. Overall, high enrichment was found for 7mer seed sequences in the 39-UTRs of genes anti-correlated with the corresponding miRNA (Figure 5), which was comparable to the enrichment patterns observed using miRanda or TargetScan predictions (which incorporate additional sequence features with seed sequence alignment); weaker enrichment patterns were found for seed sequences in coding regions, while no enrichment was evident for 59-UTRs. MiR-29a impacts anti-correlated gene targets and ovarian cancer cell viability The above results indicate widespread effects of miRNAs on gene expression in ovarian cancer, though any putative miR- NA:mRNA interactions of interest remain to be validated. There are a number of ways one could arrive at candidate miRNAs for functional studies, using any of the results presented in our study. We focused our attention here on the miR-29 family, given its PLoS ONE | www.plosone.org 5 March 2012 | Volume 7 | Issue 3 | e34546