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Comprehensive Analysis of Long Non-Coding RNAs in Ovarian Cancer Reveals Global Patterns and Targeted DNA Amplification

Overview of attention for article published in PLOS ONE, November 2013
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Title
Comprehensive Analysis of Long Non-Coding RNAs in Ovarian Cancer Reveals Global Patterns and Targeted DNA Amplification
Published in
PLOS ONE, November 2013
DOI 10.1371/journal.pone.0080306
Pubmed ID
Authors

Rozita Akrami, Anders Jacobsen, Jessica Hoell, Nikolaus Schultz, Chris Sander, Erik Larsson

Abstract

Long non-coding RNAs (lncRNAs) are emerging as potent regulators of cell physiology, and recent studies highlight their role in tumor development. However, while established protein-coding oncogenes and tumor suppressors often display striking patterns of focal DNA copy-number alteration in tumors, similar evidence is largely lacking for lncRNAs. Here, we report on a genomic analysis of GENCODE lncRNAs in high-grade serous ovarian adenocarcinoma, based on The Cancer Genome Atlas (TCGA) molecular profiles. Using genomic copy-number data and deep coverage transcriptome sequencing, we derived dual copy-number and expression data for 10,419 lncRNAs across 407 primary tumors. We describe global correlations between lncRNA copy-number and expression, and associate established expression subtypes with distinct lncRNA signatures. By examining regions of focal copy-number change that lack protein-coding targets, we identified an intergenic lncRNA on chromosome 1, OVAL, that shows narrow focal genomic amplification in a subset of tumors. While weakly expressed in most tumors, focal amplification coincided with strong OVAL transcriptional activation. Screening of 16 other cancer types revealed similar patterns in serous endometrial carcinomas. This shows that intergenic lncRNAs can be specifically targeted by somatic copy-number amplification, suggestive of functional involvement in tumor initiation or progression. Our analysis provides testable hypotheses and paves the way for further study of lncRNAs based on TCGA and other large-scale cancer genomics datasets.

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Geographical breakdown

Country Count As %
Netherlands 1 <1%
Italy 1 <1%
Unknown 109 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 29%
Student > Ph. D. Student 25 23%
Student > Master 13 12%
Professor > Associate Professor 7 6%
Student > Bachelor 6 5%
Other 13 12%
Unknown 15 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 32 29%
Agricultural and Biological Sciences 31 28%
Medicine and Dentistry 14 13%
Computer Science 6 5%
Engineering 4 4%
Other 11 10%
Unknown 13 12%