Title |
Principles Governing A-to-I RNA Editing in the Breast Cancer Transcriptome
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Published in |
Cell Reports, October 2015
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DOI | 10.1016/j.celrep.2015.09.032 |
Pubmed ID | |
Authors |
Debora Fumagalli, David Gacquer, Françoise Rothé, Anne Lefort, Frederick Libert, David Brown, Naima Kheddoumi, Adam Shlien, Tomasz Konopka, Roberto Salgado, Denis Larsimont, Kornelia Polyak, Karen Willard-Gallo, Christine Desmedt, Martine Piccart, Marc Abramowicz, Peter J. Campbell, Christos Sotiriou, Vincent Detours |
Abstract |
Little is known about how RNA editing operates in cancer. Transcriptome analysis of 68 normal and cancerous breast tissues revealed that the editing enzyme ADAR acts uniformly, on the same loci, across tissues. In controlled ADAR expression experiments, the editing frequency increased at all loci with ADAR expression levels according to the logistic model. Loci-specific "editabilities," i.e., propensities to be edited by ADAR, were quantifiable by fitting the logistic function to dose-response data. The editing frequency was increased in tumor cells in comparison to normal controls. Type I interferon response and ADAR DNA copy number together explained 53% of ADAR expression variance in breast cancers. ADAR silencing using small hairpin RNA lentivirus transduction in breast cancer cell lines led to less cell proliferation and more apoptosis. A-to-I editing is a pervasive, yet reproducible, source of variation that is globally controlled by 1q amplification and inflammation, both of which are highly prevalent among human cancers. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 8 | 47% |
Belgium | 2 | 12% |
United Kingdom | 1 | 6% |
Brazil | 1 | 6% |
Unknown | 5 | 29% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 10 | 59% |
Scientists | 5 | 29% |
Practitioners (doctors, other healthcare professionals) | 1 | 6% |
Science communicators (journalists, bloggers, editors) | 1 | 6% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | <1% |
France | 1 | <1% |
Chile | 1 | <1% |
Denmark | 1 | <1% |
United Kingdom | 1 | <1% |
Unknown | 237 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 49 | 20% |
Student > Ph. D. Student | 48 | 20% |
Student > Bachelor | 24 | 10% |
Student > Master | 19 | 8% |
Student > Doctoral Student | 15 | 6% |
Other | 37 | 15% |
Unknown | 51 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 74 | 30% |
Agricultural and Biological Sciences | 58 | 24% |
Medicine and Dentistry | 23 | 9% |
Computer Science | 8 | 3% |
Chemistry | 5 | 2% |
Other | 20 | 8% |
Unknown | 55 | 23% |