miRNA-Gene Integration in Ovarian Cancer Figure 1. In ovarian tumors, expression patterns of miRNAs (miRs) are influenced by both copy number alteration (CNA) and genomic location. (A) MiRNAs correlated with CNA. Left plot shows hemizygous loss (N, = 1 copies) versus homozygous deletion (N = 0) for miRNA precursors. Correlation (Pearson’s) between precursor DNA copy number and mature miRNA expression is indicated by symbol size, and only miRNAs with a minimum correlation (R.0.3) are included here; cytoband regions in bold represent focal CNA events. Similarly, right plot shows gain (N. = 3) versus amplification (N. = 4) for miRNAs having R.0.3 for precursor copy versus mature expression. (B) miRNAs are frequently coexpressed with neighboring miRNAs. Plot shows relationship between the distance separating miRNA loci and their coordinate expression. Each miRNA was paired with each of the others lying in the same orientation on the same chromosome. For each pair, the distance between the two loci (right axis) was ranked, and the correlation coefficient for their expression was plotted according this rank (left axis). (C) miRNAs are frequently coexpressed with host genes. For each of 188 miRNA-host gene pairs (same orientation), the correlation was computed both between miRNA and host gene expression; pair orderings are the same (‘‘X’’, no corresponding mRNA or gene copy data; p-values by two-sided t-statistic). doi:10.1371/journal.pone.0034546.g001 (with the miRNA high and the gene low specifically in the given subtype, or vice versa) (Figure 2 and Dataset S2). MiRNAs high in C1 and C2 tumors included let-7 family members (excluding let- 7b). Potential gene targets of interest included genes upregulated during epithelial-to-mesenchymal transition (EMT), such as ZEB2, MMP2, SNAI2, FN1, TWIST1, which were all high in the (mesenchymal-associated) C2 and C3 tumors; furthermore, C2 tumors had high vimentin mRNA and low E-cadherin mRNA. MiRNAs correlated in expression with worse patient outcome A set of molecular correlates of patient outcome is another resource TCGA data may provide to the research community. Previously, we identified a gene transcriptional signature predic- tive of overall survival in ovarian cancer [8]. Here, we carried out a similar analysis to define a miRNA signature of patient prognosis. In a training subset of 228 ovarian tumors (with outcome data, TCGA batches 9–15), 34 human miRNAs were individually correlated with time to death (Figure 3A, P,0.01, univariate Cox, average signal.50 units). Each of the 253 validation samples (batches 17–24) was assigned a prognostic score, reflecting the similarity between its expression profile and the prognostic miRNA signature pattern; the signature showed statistically significant associations with survival (Figure 3A, Log rank P = 0.03, miRNA risk index.0 vs ,0, and P = 0.02, univariate Cox of the prognostic score as a continuous variable). Similar attempts at defining miRNA signatures of response to platinum therapy were unsuccessful (Document S1); however, as platinum-resistance is most likely a complex and multifactorial process, a role for miRNAs in platinum-resistance cannot be ruled out. A previous study [19] identified three miRNAs being associated with outcome in ovarian cancer; two of these miRNAs, miR-337 and miR-410, were also significant (P,0.05) in our training dataset. While a previous study had described an PLoS ONE | www.plosone.org 3 March 2012 | Volume 7 | Issue 3 | e34546