Oncotarget

Research Papers:

RWCFusion: identifying phenotype-specific cancer driver gene fusions based on fusion pair random walk scoring method

Jianmei Zhao, Xuecang Li, Qianlan Yao, Meng Li, Jian Zhang, Bo Ai, Wei Liu, Qiuyu Wang, Chenchen Feng, Yuejuan Liu, Xuefeng Bai, Chao Song, Shang Li, Enmin Li, Liyan Xu and Chunquan Li _

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Oncotarget. 2016; 7:61054-61068. https://doi.org/10.18632/oncotarget.11064

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Abstract

Jianmei Zhao1,2,*, Xuecang Li1,*, Qianlan Yao3,*, Meng Li1, Jian Zhang1, Bo Ai1, Wei Liu4, Qiuyu Wang5, Chenchen Feng1, Yuejuan Liu1, Xuefeng Bai1, Chao Song5, Shang Li6, Enmin Li2, Liyan Xu2, Chunquan Li1,2

1School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China

2The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, 515041, China

3School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China

4Department of Mathematics, Heilongjiang Institute of Technology, Harbin, 150050, China

5School of Nursing and Pharmacology, Daqing Campus, Harbin Medical University, Daqing, 163319, China

6College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China

*Joint first authors

Correspondence to:

Chunquan Li, email: [email protected]

Liyan Xu, email: [email protected]

Keywords: gene fusion, cancer, network, driver

Received: March 26, 2016     Accepted: July 19, 2016     Published: August 05, 2016

ABSTRACT

While gene fusions have been increasingly detected by next-generation sequencing (NGS) technologies based methods in human cancers, these methods have limitations in identifying driver fusions. In addition, the existing methods to identify driver gene fusions ignored the specificity among different cancers or only considered their local rather than global topology features in networks. Here, we proposed a novel network-based method, called RWCFusion, to identify phenotype-specific cancer driver gene fusions. To evaluate its performance, we used leave-one-out cross-validation in 35 cancers and achieved a high AUC value 0.925 for overall cancers and an average 0.929 for signal cancer. Furthermore, we classified 35 cancers into two classes: haematological and solid, of which the haematological got a highly AUC which is up to 0.968. Finally, we applied RWCFusion to breast cancer and found that top 13 gene fusions, such as BCAS3-BCAS4, NOTCH-NUP214, MED13-BCAS3 and CARM-SMARCA4, have been previously proved to be drivers for breast cancer. Additionally, 8 among the top 10 of the remaining candidate gene fusions, such as SULF2-ZNF217, MED1-ACSF2, and ACACA-STAC2, were inferred to be potential driver gene fusions of breast cancer by us.


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