Literature DB >> 20663929

Systematic interpretation of comutated genes in large-scale cancer mutation profiles.

Yunyan Gu1, Da Yang, Jinfeng Zou, Wencai Ma, Ruihong Wu, Wenyuan Zhao, Yuannv Zhang, Hui Xiao, Xue Gong, Min Zhang, Jing Zhu, Zheng Guo.   

Abstract

By high-throughput screens of somatic mutations of genes in cancer genomes, hundreds of cancer genes are being rapidly identified, providing us abundant information for systematically deciphering the genetic changes underlying cancer mechanism. However, the functional collaboration of mutated genes is often neglected in current studies. Here, using four genome-wide somatic mutation data sets and pathways defined in various databases, we showed that gene pairs significantly comutated in cancer samples tend to distribute between pathways rather than within pathways. At the basic functional level of motifs in the human protein-protein interaction network, we also found that comutated gene pairs were overrepresented between motifs but extremely depleted within motifs. Specifically, we showed that based on Gene Ontology that describes gene functions at various specific levels, we could tackle the pathway definition problem to some degree and study the functional collaboration of gene mutations in cancer genomes more efficiently. Then, by defining pairs of pathways frequently linked by comutated gene pairs as the between-pathway models, we showed they are also likely to be codisrupted by mutations of the interpathway hubs of the coupled pathways, suggesting new hints for understanding the heterogeneous mechanisms of cancers. Finally, we showed some between-pathway models consisting of important pathways such as cell cycle checkpoint and cell proliferation were codisrupted in most cancer samples under this study, suggesting that their codisruptions might be functionally essential in inducing these cancers. All together, our results would provide a channel to detangle the complex collaboration of the molecular processes underlying cancer mechanism. (c) 2010 AACR.

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Year:  2010        PMID: 20663929     DOI: 10.1158/1535-7163.MCT-10-0022

Source DB:  PubMed          Journal:  Mol Cancer Ther        ISSN: 1535-7163            Impact factor:   6.261


  5 in total

1.  Cooperative genomic alteration network reveals molecular classification across 12 major cancer types.

Authors:  Hongyi Zhang; Yulan Deng; Yong Zhang; Yanyan Ping; Hongying Zhao; Lin Pang; Xinxin Zhang; Li Wang; Chaohan Xu; Yun Xiao; Xia Li
Journal:  Nucleic Acids Res       Date:  2016-11-29       Impact factor: 16.971

2.  ICan: an integrated co-alteration network to identify ovarian cancer-related genes.

Authors:  Yuanshuai Zhou; Yongjing Liu; Kening Li; Rui Zhang; Fujun Qiu; Ning Zhao; Yan Xu
Journal:  PLoS One       Date:  2015-03-24       Impact factor: 3.240

3.  Digitoxin Affects Metabolism, ROS Production and Proliferation in Pancreatic Cancer Cells Differently Depending on the Cell Phenotype.

Authors:  Heléne Lindholm; Katarina Ejeskär; Ferenc Szekeres
Journal:  Int J Mol Sci       Date:  2022-07-26       Impact factor: 6.208

4.  Fast randomization of large genomic datasets while preserving alteration counts.

Authors:  Andrea Gobbi; Francesco Iorio; Kevin J Dawson; David C Wedge; David Tamborero; Ludmil B Alexandrov; Nuria Lopez-Bigas; Mathew J Garnett; Giuseppe Jurman; Julio Saez-Rodriguez
Journal:  Bioinformatics       Date:  2014-09-01       Impact factor: 6.937

Review 5.  Bioengineering tools for the production of pharmaceuticals: current perspective and future outlook.

Authors:  Surendra Sarsaiya; Jingshan Shi; Jishuang Chen
Journal:  Bioengineered       Date:  2019-12       Impact factor: 3.269

  5 in total

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