Figure 2. MiRNA correlates of molecular subtype, with associated gene expression patterns. Unsupervised clustering of miRNA expression data had identified three subtypes (C1–C3) of high- miRNA-Gene Integration in Ovarian Cancer grade serous ovarian tumors. For miRNAs and genes differing significantly between the groups (t-test P,0.01, fold change.1.5, any subtype compared to the other tumors), predicted miRNA:mRNA functional pairs were defined, based on both anti-correlation in expression and predicted miRNA targeting interaction (both miRanda and TargetScan). For each miRNA:mRNA group (e.g. miRNA high/gene low in C1 versus other tumors), expression patterns are represented as heat maps (rows, miRNAs or gene transcripts; columns, profiled samples). doi:10.1371/journal.pone.0034546.g002 association between Dicer and Drosha expression levels and overall survival in ovarian cancer [20], we did not observe this association in our data (Figure S2). Similar to what was done for the subtype-specific miRNAs, miRNA:mRNA predicted pairs were defined, using the larger set of miRNAs and genes correlated with outcome (P,0.05, univariate Cox) but in opposite directions to their predicted interactors (Figure S3 and Dataset S3). In the validation cohort, we compared the miRNA-based prognostic signature scores with the gene-based prognostic signature scores generated previously [8]. The correlation between the two sets of scores was statistically significant, though not high (Pearson’s R = 0.2, P,0.001, Figure S4A). An additional set of scores obtained by averaging both miRNA and gene scores was significant by univariate Cox (P = 0.003), though a multivariate model combining the two scores gave indeterminate results, with each score trending towards significance (P, = 0.07 for each, Figure S4B). In a three-way analysis separating tumors with high or low scores (.0 or ,0, respectively) for both gene and miRNA from the rest of the tumors, there was good separation between the groups (Log rank P = 0.01), with the mixed group (high for miRNA, low for gene, or vice versa) showing an intermediate outcome as compared to the predicted worse versus better groups (Figure S4C). In conclusion, miRNA expression patterns may complement gene expression patterns in predicting survival, with further study warranted; this issue of integrating miRNA and mRNA with survival data has also been examined elsewhere, using different analytical methods [21]. Some miRNAs correlated with disease progression could conceivably have a functional role in ovarian cancer; miRNAs correlated with better patient prognosis, for example, could be considered candidate tumor suppressors. MiR-148a, one of our better prognosis miRNAs, was recently found to inhibit prolifer- ation in ovarian cancer cells [22]. With an aim towards uncovering new candidates for therapeutic targeting, we over-expressed miR- 26b in vitro, which, while not our top significant miRNA, was correlated with better prognosis (P,0.05), as well as previously shown as inducing apoptosis in breast cancer [23]. Interestingly, miR-26b inhibited proliferation of HEYA8 but not OVCAR-8 cells (Figure S5, both cell lines being derived from ovarian cancers), though cell line-specific effects of miRNAs in ovarian cancer have been reported previously (possibly reflecting genotypic differences) [10]. One other miRNA from our signature that we tested, miR-146a, had no effect in either HEYA8 or OVCAR-8 (data not shown); no other miRNAs from this signature were tested in vitro by our group. MiRNAs and their predicted gene targets tend to be anti- correlated within ovarian tumors A key to studying miRNAs is identifying their gene targets. While miRNA targeting predictions made in silico (the vast majority being unvalidated) may have sizable rates of false positives and negatives, we hypothesized that considering correlations between gene and miRNA expression across a large panel of tumors could provide further support for potential PLoS ONE | www.plosone.org 4 March 2012 | Volume 7 | Issue 3 | e34546