Title |
Comprehensive analysis of the genome transcriptome and proteome landscapes of three tumor cell lines
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Published in |
Genome Medicine, November 2012
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DOI | 10.1186/gm387 |
Pubmed ID | |
Authors |
Pelin Akan, Andrey Alexeyenko, Paul Igor Costea, Lilia Hedberg, Beata Werne Solnestam, Sverker Lundin, Jimmie Hällman, Emma Lundberg, Mathias Uhlén, Joakim Lundeberg |
Abstract |
We here present a comparative genome, transcriptome and functional network analysis of three human cancer cell lines (A431, U251MG and U2OS), and investigate their relation to protein expression. Gene copy numbers significantly influenced corresponding transcript levels; their effect on protein levels was less pronounced. We focused on genes with altered mRNA and/or protein levels to identify those active in tumor maintenance. We provide comprehensive information for the three genomes and demonstrate the advantage of integrative analysis for identifying tumor-related genes amidst numerous background mutations by relating genomic variation to expression/protein abundance data and use gene networks to reveal implicated pathways. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 2 | 18% |
Denmark | 1 | 9% |
Montenegro | 1 | 9% |
France | 1 | 9% |
Sweden | 1 | 9% |
United Kingdom | 1 | 9% |
United States | 1 | 9% |
Unknown | 3 | 27% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 5 | 45% |
Scientists | 5 | 45% |
Science communicators (journalists, bloggers, editors) | 1 | 9% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Sweden | 2 | 2% |
Korea, Republic of | 1 | <1% |
India | 1 | <1% |
United Kingdom | 1 | <1% |
United States | 1 | <1% |
Unknown | 107 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 30 | 27% |
Researcher | 24 | 21% |
Student > Master | 17 | 15% |
Student > Bachelor | 11 | 10% |
Professor > Associate Professor | 7 | 6% |
Other | 16 | 14% |
Unknown | 8 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 42 | 37% |
Biochemistry, Genetics and Molecular Biology | 32 | 28% |
Medicine and Dentistry | 12 | 11% |
Computer Science | 6 | 5% |
Pharmacology, Toxicology and Pharmaceutical Science | 3 | 3% |
Other | 11 | 10% |
Unknown | 7 | 6% |