miRNA-Gene Integration in Ovarian Cancer Figure 6. MiR-29a impacts anti-correlated gene targets and ovarian cancer cell viability. (A) Correlation of gene expression with miR-29a expression (genes from Figure 4B, same ordering). Predicted miR-29a targets are indicated. (B) Top eight words (of all 5, 6 and 7mers) enriched in 39- UTRs of mRNAs anti-correlated with miR-29a expression (FDR,1e-6). (C) QPCR analysis showing relative quantity of selected miR-29a anti-correlated gene targets after miR-29a overexpression in HEYA8 ovarian cancer cells (SCR, scrambled control; WT, untreated; two-sided t-test P,0.05, mir-29a vs SCR and miR-29a vs WT, each comparison, except for SAE1). (D) MTS assays demonstrating the effect of miR-29a overexpression on proliferation of HEYA8 and OVCAR-8 cells (Lipo, lipofectamine-treated alone, no miRNA; two-sided t-test P,0.001, miR-29a vs each of three control groups at both 48 h and 72 h). (E) Effect of miR-29a (72 h) on proliferation, under a range of concentrations of cisplatin treatment (at 48 h). Error bars represent standard error. doi:10.1371/journal.pone.0034546.g006 integrated approaches to discovering novel candidate targets for cancer therapy in ovarian as well as other cancers. Materials and Methods Molecular profiling datasets The set of 487 tumors analyzed were from the original TCGA set of 489 [8] (samples TCGA-04-1536 and TCGA-61-1911 did not have quality miRNA data at the time of this study). The miRNA array normalization steps are as follows. The gMean- Signal from raw array files (‘‘level 1’’) were quantile normalized and log transformed, removing duplicate samples and control probes (‘‘level 2’’). Multiple median centering steps set the median of every batch to the median of all batches: in brief, within each batch, we first subtracted the median for each miRNA, then calculated the across batch median and added it back to all samples within that batch; the resulting data were collapsed to miRNA levels (‘‘level 3’’). The level 3 miRNA data are available at the TCGA Data Portal [32]. For gene expression analysis, we relied on the previously described ‘‘unified’’ dataset [8]. miRNA:mRNA correlation analysis Differentially expressed genes and miRNAs were identified using two-sided t-test on log-transformed data. Java TreeView [33] represented expression patterns as color maps. MiR- NA:mRNA targeting relationships for both subtype correlates and patient survival correlates were identified using TargetScan Human (release 5.0) [7] and miRanda (September 2008) [34]; SigTerms facilitated retrieval of putative miRNA:mRNA pairs [35]. Predicted targeting relationships for miRNA:mRNA corre- lations were identified using miRanda (microRNA.org, August 2010, conserved set). For estimating absolute miRNA precursor copy levels (Figure 1A), we used the cBio Cancer Genomics Portal [36]. The top 191 miRNAs X 8547 genes (Figures 4 and 5) were defined as: for miRNAs, those in the top 100 with highest signal in at least ten individual samples; for genes, with expression above the tumor sample median in at least ten samples. Using a previously-described statistical framework evaluating correlation of 39-UTR oligonucelotide (word) occurrences and mRNA expres- sion changes [37], we analyzed motifs enriched in 39-UTRs of mRNAs anti-correlated with miR-29a expression, computing word association z-score with P-value for all 21,504 words of length 5–7. Survival analysis The definition and validation of a prognostic miRNA signature was carried out essentially as described for the previously-defined prognostic mRNA (gene) signature [8], using the previously- defined training and validation subsets with expression values normalized within each subset to standard deviations from the median. Given the miRNA signature from the training dataset, the prognostic t-score was defined for each validation profile as the two-sided t-statistic comparing, within each tumor profile, the average of the poor prognosis miRNAs with the average of the good prognosis miRNAs. Cell cultures OVCAR-8 cells were obtained from the NCI-Frederick Cancer DCTD Tumor/Cell Line Repository (Frederick, MD), and HEY- A8 cells were obtained from Gordon Mills (M.D. Anderson), both cell lines having been properly authenticated by their respective sources; cells were passaged in our laboratory for no more than two months after resuscitation. Cells were cultured in RPMI 1640 (Gibco) with 10% heat-inactivated fetal bovine serum (Denville Scientific) and penicillin-streptomycin (Invitrogen). miR-29a overexpression Cell lines were transfected in 6-well plates (66104 cells/well) using 2.5 ml/well of Lipofectamine 2000 Transfection Reagent (Invitrogen) and 10 pmol of hsa-miR29a (Ambion), according to manufacturer’s instructions. Control groups of cells were treated with transfection reagent alone (mock transfection), or transfected with pre-miR negative control #1 (Ambion). Proliferation assays Cell were seeded (3000/well) in a 96 well plate and transfected using 0.1 ml/well of Lipofectamine 2000 Transfection Reagent (Invitrogen) and 0.5 pmol of hsa-miR29a (Ambion), with lipofecta- mine alone or pre-miR negative control #1 (Ambion) as controls. Cell proliferation was assayed at 24, 48, and 72 hrs post-transfection using the MTS-based CellTiter 96 cell proliferation assay (Promega, Madison, WI). The time course MTS assay experiments were run three times (separate days), each with a different set of biological quadruplicates (n = 12 per group); within each experiment run, the viability measures within each time point were centered on the mean of the WT group for the first run. For cisplatin treatment, cells were transfected as described above, and media was replaced after 24 hrs with media containing Cisplatin (Sigma) (0–7.5 mg/mL); viability was assayed 72 hrs post-transfection; experiments were run three times, each with a different biological replicate (n = 3 per group); for each run, viability measures within each concentration point were centered on the mean of values for the first run. Quantitative real-time PCR (QPCR) Total RNA (60 ng) was reverse transcribed in a 40 ml reaction using the TaqManH MicroRNA Reverse Transcription Kit (ABI). Custom primer sequences are in Table S2. QPCR was performed on a StepOne Real-Time PCR System (ABI) using Power- SYBR Green PCR Master Mix (ABI) in a 20 ml reaction and human ribosomal RNA 18 s as an endogenous control (which was itself not miR-29a- regulated, data not shown). The QPCR experiments were run four times (separate days), each with independent biological samples (n = 4 per group); within each experiment run, relative expression values were normalized to standard deviations from the mean. Supporting Information Dataset S1 miRNAs most correlated in expression with DNA copy number. (XLSX) PLoS ONE | www.plosone.org 9 March 2012 | Volume 7 | Issue 3 | e34546