|Title||Integrated analyses of microRNAs demonstrate their widespread influence on gene expression in high-grade serous ovarian carcinoma.|
|Publication Type||Journal Article|
|Year of Publication||2012|
|Authors||Creighton, CJ, Hernandez-Herrera, A, Jacobsen, A, Levine, DA, Mankoo, P, Schultz, N, Du, Y, Zhang, Y, Larsson, E, Sheridan, R, Xiao, W, Spellman, PT, Getz, G, Wheeler, DA, Perou, CM, Gibbs, RA, Sander, C, D Hayes, N, Gunaratne, PH|
|Keywords||3' Untranslated Regions, Breast, Cell Line, Tumor, DNA Copy Number Variations, Female, Gene Expression Regulation, Neoplastic, Humans, MicroRNAs, Neoplasms, Cystic, Mucinous, and Serous, Ovarian Neoplasms|
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 3'-untranslated region (with less frequency observed for coding sequence and 5'-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.
|Alternate Journal||PLoS ONE|
|PubMed Central ID||PMC3315571|
|Grant List||P30 CA016086 / CA / NCI NIH HHS / United States |
P30CA125123 / CA / NCI NIH HHS / United States
U24CA143840 / CA / NCI NIH HHS / United States
U24CA143843 / CA / NCI NIH HHS / United States
U24CA143848 / CA / NCI NIH HHS / United States
U54 HG003273 / HG / NHGRI NIH HHS / United States