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Sci Rep. 2013 Dec 18;3:3538. doi: 10.1038/srep03538.

Identifying potential cancer driver genes by genomic data integration.

Author information

1
1] National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China [2] MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China.
2
MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China.
3
School of Applied Mathematics, Central University of Finance and Economics, Beijing 102206, China.
4
1] MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China [2] Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA.

Abstract

Cancer is a genomic disease associated with a plethora of gene mutations resulting in a loss of control over vital cellular functions. Among these mutated genes, driver genes are defined as being causally linked to oncogenesis, while passenger genes are thought to be irrelevant for cancer development. With increasing numbers of large-scale genomic datasets available, integrating these genomic data to identify driver genes from aberration regions of cancer genomes becomes an important goal of cancer genome analysis and investigations into mechanisms responsible for cancer development. A computational method, MAXDRIVER, is proposed here to identify potential driver genes on the basis of copy number aberration (CNA) regions of cancer genomes, by integrating publicly available human genomic data. MAXDRIVER employs several optimization strategies to construct a heterogeneous network, by means of combining a fused gene functional similarity network, gene-disease associations and a disease phenotypic similarity network. MAXDRIVER was validated to effectively recall known associations among genes and cancers. Previously identified as well as novel driver genes were detected by scanning CNAs of breast cancer, melanoma and liver carcinoma. Three predicted driver genes (CDKN2A, AKT1, RNF139) were found common in these three cancers by comparative analysis.

PMID:
24346768
PMCID:
PMC3866686
DOI:
10.1038/srep03538
[Indexed for MEDLINE]
Free PMC Article

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