Integrated Analyses of microRNAs Demonstrate Their Widespread Influence on Gene Expression in High-Grade Serous Ovarian Carcinoma Chad J. Creighton1,2*., Anadulce Hernandez-Herrera11., Anders Jacobsen4., Douglas A. Levine3, Parminder Mankoo 4 , Nikolaus Schultz4, Ying Du 5 , Yiqun Zhang1, Erik Larsson 4 , Robert Sheridan 4 , Weimin Xiao11, Paul T. Spellman 7 , Gad Getz8, David A. Wheeler9, Charles M. Perou 5 , Richard A. Gibbs9, Chris Sander4, D. Neil Hayes5,6, Preethi H. Gunaratne10,11*, The Cancer Genome Atlas Research Network 1 Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, Texas, United States of America , 2 Department of Medicine, Baylor College of Medicine, Houston, Texas, United States of America, 3 Gynecology Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America, 4 Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America, 5 Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, United States of America, 6 Department of Internal Medicine, Division of Medical Oncology, University of North Carolina, Chapel Hill, North Carolina, United States of America, 7 Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America, 8 Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America, 9 Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America, 10 Department of Pathology, Baylor College of Medicine, Houston, Texas, United States of America, 11 Department of Biology & Biochemistry, University of Houston, Houston, Texas, United States of America Abstract Background: The Cancer Genome Atlas (TCGA) Network recently comprehensively catalogued the molecular aberrations in 487 high-grade serous ovarian cancers, with much remaining to be elucidated regarding the microRNAs (miRNAs). Here, using TCGA ovarian data, we surveyed the miRNAs, in the context of their predicted gene targets. Methods and Results: Integration of miRNA and gene patterns yielded evidence that proximal pairs of miRNAs are processed from polycistronic primary transcripts, and that intronic miRNAs and their host gene mRNAs derive from common transcripts. Patterns of miRNA expression revealed multiple tumor subtypes and a set of 34 miRNAs predictive of overall patient survival. In a global analysis, miRNA:mRNA pairs anti-correlated in expression across tumors showed a higher frequency of in silico predicted target sites in the mRNA 39-untranslated region (with less frequency observed for coding sequence and 59-untranslated regions). The miR-29 family and predicted target genes were among the most strongly anti- correlated miRNA:mRNA pairs; over-expression of miR-29a in vitro repressed several anti-correlated genes (including DNMT3A and DNMT3B) and substantially decreased ovarian cancer cell viability. Conclusions: This study establishes miRNAs as having a widespread impact on gene expression programs in ovarian cancer, further strengthening our understanding of miRNA biology as it applies to human cancer. As with gene transcripts, miRNAs exhibit high diversity reflecting the genomic heterogeneity within a clinically homogeneous disease population. Putative miRNA:mRNA interactions, as identified using integrative analysis, can be validated. TCGA data are a valuable resource for the identification of novel tumor suppressive miRNAs in ovarian as well as other cancers. Citation: Creighton CJ, Hernandez-Herrera A, Jacobsen A, Levine DA, Mankoo P, et al. (2012) Integrated Analyses of microRNAs Demonstrate Their Widespread Influence on Gene Expression in High-Grade Serous Ovarian Carcinoma. PLoS ONE 7(3): e34546. doi:10.1371/journal.pone.0034546 Editor: Aed´ın C. Culhane, Harvard School of Public Health, United States of America Received January 10, 2012; Accepted March 1, 2012; Published March 29, 2012 Copyright: 2012 Creighton et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported in part by the Ovarian Cancer Research Fund (CJC, PHG), National Institutes of Health (NIH) grant P30CA125123 (CJC, YZ), TCGA GCC at Baylor: National Cancer Institue (NCI) grant U24CA143843 (CJC, DAW, RAG), TCGA GCC at University of North Carolina: NCI-U24CA143848 (YD, CMP, DNH), TCGA GDAC at Memorial Sloan-Kettering Cancer Center: NCI-U24CA143840 (AJ, PM, NS, EL, RS, CS), CPRIT-RP110355 (PHG), a Cullen Foundation Grant (PHG), the Mexican National Council for Science and Technology (CONACyT) (AH), and the Danish Research Council (AJ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: CJC serves as an Academic Editor for PLoS ONE. This does not alter the authors’ adherence to all the PLoS ONE policies on sharing data and materials. * E-mail: creighto@bcm.edu (CJC); phgunaratne@uh.edu (PHG) . These authors contributed equally to this work. Introduction MicroRNAs (miRNAs or miRs) are ,22 nt noncoding RNAs which target complementary gene transcripts for translational repression or mRNA cleavage [1]. Having been implicated in the initiation and progression of human cancers, miRNAs regulate processes such as cell growth, differentiation, and apoptosis [2]. A productive miRNA:mRNA interaction can occur with as little as six consecutive nucleotides, through pairing between the 59-seed of the miRNA (located in nucleotides 2–7) and sequences which are PLoS ONE | www.plosone.org 1 March 2012 | Volume 7 | Issue 3 | e34546