Identifying potential cancer driver genes by genomic data integration
作者： Yong Chen , Jingjing Hao , Wei Jiang , Tong He , Xuegong Zhang
DOI: 10.1038/SREP03538
关键词: CDKN2A 、 Gene mutation 、 Genomic library 、 Cancer 、 Carcinogenesis 、 Biology 、 Phenotype 、 Genome 、 Gene 、 Genetics
摘要: Cancer is a genomic disease associated with plethora of gene mutations resulting in loss control over vital cellular functions. Among these mutated genes, driver genes are defined as being causally linked to oncogenesis, while passenger thought be irrelevant for cancer development. With increasing numbers large-scale datasets available, integrating data identify from aberration regions genomes becomes an important goal genome analysis and investigations into mechanisms responsible A computational method, MAXDRIVER, proposed here potential on the basis copy number (CNA) genomes, by publicly available human data. MAXDRIVER employs several optimization strategies construct heterogeneous network, means combining fused functional similarity gene-disease associations phenotypic network. was validated effectively recall known among cancers. Previously identified well novel were detected scanning CNAs breast cancer, melanoma liver carcinoma. Three predicted (CDKN2A, AKT1, RNF139) found common three cancers comparative analysis.
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