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RWCFusion: identifying phenotype-specific cancer driver gene fusions based on fusion pair random walk scoring method
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...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals LLC
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5308635/ https://www.ncbi.nlm.nih.gov/pubmed/27506935 http://dx.doi.org/10.18632/oncotarget.11064 |
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author | Zhao, Jianmei Li, Xuecang Yao, Qianlan Li, Meng Zhang, Jian Ai, Bo Liu, Wei Wang, Qiuyu Feng, Chenchen Liu, Yuejuan Bai, Xuefeng Song, Chao Li, Shang Li, Enmin Xu, Liyan Li, Chunquan |
author_facet | Zhao, Jianmei Li, Xuecang Yao, Qianlan Li, Meng Zhang, Jian Ai, Bo Liu, Wei Wang, Qiuyu Feng, Chenchen Liu, Yuejuan Bai, Xuefeng Song, Chao Li, Shang Li, Enmin Xu, Liyan Li, Chunquan |
author_sort | Zhao, Jianmei |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5308635 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Impact Journals LLC |
record_format | MEDLINE/PubMed |
spelling | pubmed-53086352017-03-09 RWCFusion: identifying phenotype-specific cancer driver gene fusions based on fusion pair random walk scoring method Zhao, Jianmei Li, Xuecang Yao, Qianlan Li, Meng Zhang, Jian Ai, Bo Liu, Wei Wang, Qiuyu Feng, Chenchen Liu, Yuejuan Bai, Xuefeng Song, Chao Li, Shang Li, Enmin Xu, Liyan Li, Chunquan Oncotarget Research Paper 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. Impact Journals LLC 2016-08-05 /pmc/articles/PMC5308635/ /pubmed/27506935 http://dx.doi.org/10.18632/oncotarget.11064 Text en Copyright: © 2016 Zhao et al. http://creativecommons.org/licenses/by/2.5/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Paper Zhao, Jianmei Li, Xuecang Yao, Qianlan Li, Meng Zhang, Jian Ai, Bo Liu, Wei Wang, Qiuyu Feng, Chenchen Liu, Yuejuan Bai, Xuefeng Song, Chao Li, Shang Li, Enmin Xu, Liyan Li, Chunquan RWCFusion: identifying phenotype-specific cancer driver gene fusions based on fusion pair random walk scoring method |
title | RWCFusion: identifying phenotype-specific cancer driver gene fusions based on fusion pair random walk scoring method |
title_full | RWCFusion: identifying phenotype-specific cancer driver gene fusions based on fusion pair random walk scoring method |
title_fullStr | RWCFusion: identifying phenotype-specific cancer driver gene fusions based on fusion pair random walk scoring method |
title_full_unstemmed | RWCFusion: identifying phenotype-specific cancer driver gene fusions based on fusion pair random walk scoring method |
title_short | RWCFusion: identifying phenotype-specific cancer driver gene fusions based on fusion pair random walk scoring method |
title_sort | rwcfusion: identifying phenotype-specific cancer driver gene fusions based on fusion pair random walk scoring method |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5308635/ https://www.ncbi.nlm.nih.gov/pubmed/27506935 http://dx.doi.org/10.18632/oncotarget.11064 |
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