miRNA-Gene Integration in Ovarian Cancer Figure 4. Correlations between miRNAs and genes in ovarian cancer. (A) MiRNAs and their predicted gene targets tend to be anti-correlated within ovarian tumors. Scatter plot showing mean correlation and fraction of predicted target interactions (miRanda-mirSVR score.0.1), using bins of 10000 miRNA:mRNA pairs (total number of pairs represented: 191 miRNAs X 8547 genes). Dashed line corresponds to chance expected baseline fraction (13.2%) of predicted target interactions. Correlations were computed using both Pearson’s (gray line) and a simple linear regression model to account for ‘noise’ due to DNA copy alterations (black line). (B) Hierarchical clustering matrix (with Pearson’s correlation coefficient as distance metric, Ward’s Linkage) of correlation coefficients for all miRNA:mRNA pairs having a strong negative correlation (regression coefficient smaller than 27.0, only negative correlations represented). For each gene cluster, enriched gene classes are indicated. *, significant anti-enrichment (P,0.001, one-sided Spearman’s rank, TargetScan or miRanda) for predicted targets within miRNA:mRNA correlations. doi:10.1371/journal.pone.0034546.g004 strong anti-correlation with many cell cycle-related genes (Figure 4B). Members of the miR-29 family have been demonstrated to act as tumor suppressors in acute myeloid leukemia and lung cancer, in part by reverting aberrant methylation patterns through its targeting of DNA methyltrans- ferases (DNMT) and methylation-silenced tumor suppressors [28,29]. Top anti-correlated genes of miR-29 in ovarian cancer included DNMT3A and DNMT3B (Figure 6A), suggesting a similar role for miR-29 in high-grade serous ovarian cancer. MiR-29a was under-expressed and DNMT3A mRNA was over-expressed in the DNA methylation subtype ‘‘MC2’’ (Figure S11); furthermore, possible targets of DNMT3A methylation in the ovarian tumors (having DNA methylation levels correlated with DNMT3A expression) were enriched for genes showing an impact by methylation (Figure S12, Table S1). Genes anti-correlated with miR-29a were enriched for miR-29a targets as predicted by sequence analysis (either TargetScan or miRanda, Figure 6A). However, many in silico predicted targets did not show the anticipated anti-correlation patterns, again suggesting that by factoring in expression data, we could reduce PLoS ONE | www.plosone.org 6 March 2012 | Volume 7 | Issue 3 | e34546