Title |
Biomolecular Events in Cancer Revealed by Attractor Metagenes
|
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Published in |
PLoS Computational Biology, February 2013
|
DOI | 10.1371/journal.pcbi.1002920 |
Pubmed ID | |
Authors |
Wei-Yi Cheng, Tai-Hsien Ou Yang, Dimitris Anastassiou |
Abstract |
Mining gene expression profiles has proven valuable for identifying signatures serving as surrogates of cancer phenotypes. However, the similarities of such signatures across different cancer types have not been strong enough to conclude that they represent a universal biological mechanism shared among multiple cancer types. Here we present a computational method for generating signatures using an iterative process that converges to one of several precise attractors defining signatures representing biomolecular events, such as cell transdifferentiation or the presence of an amplicon. By analyzing rich gene expression datasets from different cancer types, we identified several such biomolecular events, some of which are universally present in all tested cancer types in nearly identical form. Although the method is unsupervised, we show that it often leads to attractors with strong phenotypic associations. We present several such multi-cancer attractors, focusing on three that are prominent and sharply defined in all cases: a mesenchymal transition attractor strongly associated with tumor stage, a mitotic chromosomal instability attractor strongly associated with tumor grade, and a lymphocyte-specific attractor. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 2 | 50% |
France | 1 | 25% |
Unknown | 1 | 25% |
Demographic breakdown
Type | Count | As % |
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Scientists | 2 | 50% |
Members of the public | 1 | 25% |
Science communicators (journalists, bloggers, editors) | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 7 | 5% |
Sweden | 2 | 1% |
Japan | 2 | 1% |
United Kingdom | 2 | 1% |
Malaysia | 1 | <1% |
Ukraine | 1 | <1% |
Canada | 1 | <1% |
Australia | 1 | <1% |
Spain | 1 | <1% |
Other | 3 | 2% |
Unknown | 127 | 86% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 45 | 30% |
Researcher | 43 | 29% |
Student > Master | 14 | 9% |
Professor > Associate Professor | 10 | 7% |
Student > Bachelor | 8 | 5% |
Other | 18 | 12% |
Unknown | 10 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 56 | 38% |
Computer Science | 21 | 14% |
Biochemistry, Genetics and Molecular Biology | 16 | 11% |
Medicine and Dentistry | 16 | 11% |
Engineering | 11 | 7% |
Other | 11 | 7% |
Unknown | 17 | 11% |