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Am J Cancer Res. 2014 Jul 16;4(4):394-410. eCollection 2014.

Genome-wide prediction of cancer driver genes based on SNP and cancer SNV data.

Author information

  • 1The State Key Laboratory of Genetic Engineering, Institute of Biomedical Science, Fudan University 220 Handan Rd, Shanghai 200433, China.
  • 2Verna and Marrs Mclean Department of Biochemistry and Molecular Biology, Baylor College of Medicine One Baylor Plaza, Houston, TX 77030, USA.
  • 3Department of Chemistry, Fudan University Shanghai 200032, China ; Institute of Biomedical Sciences, Fudan University Shanghai 200032, China.
  • 4The State Key Laboratory of Genetic Engineering, Department of Genetics, Fudan University 220 Handan Rd, Shanghai 200433, China.
  • 5Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, College of Bioscience and Biotechnology, Yangzhou University Yangzhou 225009, China.


Identifying cancer driver genes and exploring their functions are essential and the most urgent need in basic cancer research. Developing efficient methods to differentiate between driver and passenger somatic mutations revealed from large-scale cancer genome sequencing data is critical to cancer driver gene discovery. Here, we compared distinct features of SNP with SNV data in detail and found that the weighted ratio of SNV to SNP (termed as WVPR) is an excellent indicator for cancer driver genes. The power of WVPR was validated by accurate predictions of known drivers. We ranked most of human genes by WVPR and did functional analyses on the list. The results demonstrate that driver genes are usually highly enriched in chromatin organization related genes/pathways. And some protein complexes, such as histone acetyltransferase, histone methyltransferase, telomerase, centrosome, sin3 and U12-type spliceosomal complexes, are hot spots of driver mutations. Furthermore, this study identified many new potential driver genes (e.g. NTRK3 and ZIC4) and pathways including oxidative phosphorylation pathway, which were not deemed by previous methods. Taken together, our study not only developed a method to identify cancer driver genes/pathways but also provided new insights into molecular mechanisms of cancer development.


Bioinformatics; SNP; SNV; cancer driver gene; mutation frequency

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