Title |
Identification of novel prostate cancer drivers using RegNetDriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network
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Published in |
Genome Biology, July 2017
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DOI | 10.1186/s13059-017-1266-3 |
Pubmed ID | |
Authors |
Priyanka Dhingra, Alexander Martinez-Fundichely, Adeline Berger, Franklin W. Huang, Andre Neil Forbes, Eric Minwei Liu, Deli Liu, Andrea Sboner, Pablo Tamayo, David S. Rickman, Mark A. Rubin, Ekta Khurana |
Abstract |
We report a novel computational method, RegNetDriver, to identify tumorigenic drivers using the combined effects of coding and non-coding single nucleotide variants, structural variants, and DNA methylation changes in the DNase I hypersensitivity based regulatory network. Integration of multi-omics data from 521 prostate tumor samples indicated a stronger regulatory impact of structural variants, as they affect more transcription factor hubs in the tissue-specific network. Moreover, crosstalk between transcription factor hub expression modulated by structural variants and methylation levels likely leads to the differential expression of target genes. We report known prostate tumor regulatory drivers and nominate novel transcription factors (ERF, CREB3L1, and POU2F2), which are supported by functional validation. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 10 | 33% |
United Kingdom | 2 | 7% |
Switzerland | 2 | 7% |
Israel | 1 | 3% |
Singapore | 1 | 3% |
India | 1 | 3% |
France | 1 | 3% |
Denmark | 1 | 3% |
Germany | 1 | 3% |
Other | 4 | 13% |
Unknown | 6 | 20% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 18 | 60% |
Members of the public | 11 | 37% |
Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 74 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 27 | 36% |
Researcher | 16 | 22% |
Student > Master | 8 | 11% |
Student > Bachelor | 7 | 9% |
Professor | 4 | 5% |
Other | 7 | 9% |
Unknown | 5 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 33 | 45% |
Agricultural and Biological Sciences | 15 | 20% |
Medicine and Dentistry | 6 | 8% |
Computer Science | 5 | 7% |
Engineering | 3 | 4% |
Other | 3 | 4% |
Unknown | 9 | 12% |