miRNA-Gene Integration in Ovarian Cancer Figure 5. Gene transcripts with miRNA 7mer in the 39-UTR tend to be anti-correlated with expression of the corresponding miRNA. Analogous to Figure 4A, scatter plot showing mean correlation versus significance of enrichment for predicted target interactions (enrichment expressed as a Fisher’s exact z-score), when separately considering the following potential interactions: 7mer seed sequence in 39-UTR (black dotted line), 7mer seed sequence in 59-UTR (black dashed line), 7mer seed sequence in coding sequence region (‘‘cds,’’ gray dotted line), miRanda prediction (black solid line), TargetScan prediction (gray solid line). Plot uses bins of 10000 miRNA:mRNA pairs (total number of pairs represented: 191 miRNAs X 8547 genes). Fisher’s exact z-score of +/22.57 corresponds to significant enrichment (nominal P,0.01) for predicted targets within miRNA:mRNA pairs. doi:10.1371/journal.pone.0034546.g005 the false positive rate for target predictions. Furthermore, as additional evidence for miR-29 activity, a correlation-based sequence motif analysis found that the miR-29 seed sequence complement was the top enriched motif in 39-UTRs of mRNAs anti-correlated with miR-29a expression (Figure 6B), further suggesting that miR-29 directly regulates expression levels of many target mRNAs in the tumors; this analysis also showed strong enrichment for non-canonical miR-29a seed motifs (i.e. motifs not following the typical pattern of nucleotides 2–7) with a bulge in position 3 of the miR-29a sequence, suggesting that target prediction methods requiring perfect base pairing in the seed region of the miRNA target duplex could miss a substantial fraction of functional miRNA target interactions. By forcing miR-29a expression in vitro in the ovarian cancer cell line HEYA8, we confirmed that a number of the genes anti- correlated with miR-29a—DNMT3A, DNMT3B, CDC6, CBX1, MYBL2, and TIMELESS (four of which were predicted direct targets)–were repressed by miR-29a (Figure 6C), which demon- strated these gene targets as relevant in both the in vitro functional models as well as the human tumor specimens; one gene tested, SAE1, showed anti-correlations but no functional repression. While miR-29 expression was not associated with survival (P.0.05, univariate Cox), forced miR-29a expression impacted cell proliferation in OvCar-8 and HEYA8 cell lines (Figure 6D) and had an additional effect on chemotherapeutic agent cisplatin in inhibiting the growth of these lines (Figure 6E). Discussion Molecular profiling of cancers is thought to potentially provide important new classifications of patients and insight into observed heterogeneity within a given disease. Over ten years ago, global gene expression analyses of breast cancer defined distinct molecular subtypes, which encompassed much of the known heterogeneity of the disease in terms of histology [30]. Recently, gene expression profiles have sub-classified cancers such as glioblastoma and high-grade serous ovarian cancer [8,31]; diseases which might be considered relatively homogeneous from a clinical standpoint actually show highly diverse patterns at the gene transcript level. In our study, we see extensive diversity of miRNA patterns within high grade serous ovarian cancer, suggestive of disease subtypes and patient outcome differences. We might expect these diverse miRNA patterns to be reflected in the gene expression data. Here, correlative analyses between mRNAs and miRNAs helped establish aspects of normal miRNA biology, such as the influence of host gene expression on intronic miRNAs, as being maintained in cancer. Before we began our study, it was unclear whether a broad analysis of miRNAs would show them to have a widespread impact on gene expression programs in ovarian cancer, as we have now established through the clear patterns of anti-correlation observed between miRNAs and predicted targets. Numerous functional miRNA-gene targeting relationships have been validat- ed in previous studies, using cell lines, and artificially modulating certain miRNAs in vitro can show clear effects on cell behavior. However, an overarching question with regards to cell line studies is whether these are truly relevant to human cancers. While data from experimental models (such as cell lines) help to establish cause-and-effect relationships in the laboratory, data from human tissues (such as cancer) can establish correlative (though not necessarily causal) relationships that arguably appear relevant to human disease; the combination of experimental and human tissue data therefore ought to provide the strongest support for the disease-specific relevance of a particular miRNA and its gene targets. The integration of miRNA and gene expression data within the same large panel of tumors allows us to define miRNA:mRNA correlations that are indicative of miRNA targeting. The observed enrichment of in silico predicted miRNA targets within anti- correlated miRNA:mRNA pairs both helps strengthen our confidence in the in silico predictions (as based on the canonical rules of miRNA:mRNA interaction), as well as allowing us to prioritize those predictions that appear most relevant in ovarian cancer. We should note, however, that our overall findings are broad, allowing individual exceptions to the general rule (e.g. 59- UTR targeting or miRNA-mediated upregulation of genes), and that our study does not rule out future discoveries regarding new rules of miRNA behavior not covered by our basic assumptions; furthermore, a whole gene array analysis is perhaps limited in being unable to detect alternative transcripts of the same gene differing in 39-UTRs. Notwithstanding its limitations, our analytical approach allowed us to identify miR-29a, previously showing tumor-suppressive effects in other cancers, as having a similar role in ovarian cancer. More functionally-relevant miRNAs and their targets remain to be identified and explored, and TCGA data will remain a valuable resource for miRNA:mRNA PLoS ONE | www.plosone.org 7 March 2012 | Volume 7 | Issue 3 | e34546