Identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network
Abstract Background Cancer as a kind of genomic alteration disease each year deprives many people’s life. The biggest challenge to overcome cancer is to identify driver genes that promote the cancer development from a huge amount of passenger mutations that have no effect on the selective growth adv...
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doaj-045de69db63e454f8e5a5c6746085e032021-04-02T20:54:42ZengBMCBMC Medical Genomics1755-87942019-12-0112S711210.1186/s12920-019-0619-zIdentifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene networkJunrong Song0Wei Peng1Feng Wang2Jianxin Wang3Faculty of Management and Economics/Faculty of Information Engineering and Automation/Technology Application Key Lab of Yunnan Province, Kunming University of Science and TechnologyFaculty of Management and Economics/Faculty of Information Engineering and Automation/Technology Application Key Lab of Yunnan Province, Kunming University of Science and TechnologyFaculty of Management and Economics/Faculty of Information Engineering and Automation/Technology Application Key Lab of Yunnan Province, Kunming University of Science and TechnologySchool of Information Science and Engineering, Central South UniversityAbstract Background Cancer as a kind of genomic alteration disease each year deprives many people’s life. The biggest challenge to overcome cancer is to identify driver genes that promote the cancer development from a huge amount of passenger mutations that have no effect on the selective growth advantage of cancer. In order to solve those problems, some researchers have started to focus on identification of driver genes by integrating networks with other biological information. However, more efforts should be needed to improve the prediction performance. Methods Considering the facts that driver genes have impact on expression of their downstream genes, they likely interact with each other to form functional modules and those modules should tend to be expressed similarly in the same tissue. We proposed a novel model named by DyTidriver to identify driver genes through involving the gene dysregulated expression, tissue-specific expression and variation frequency into the human functional interaction network (e.g. human FIN). Results This method was applied on 974 breast, 316 prostate and 230 lung cancer patients. The consequence shows our method outperformed other five existing methods in terms of Fscore, Precision and Recall values. The enrichment and cociter analysis illustrate DyTidriver can not only identifies the driver genes enriched in some significant pathways but also has the capability to figure out some unknown driver genes. Conclusion The final results imply that driver genes are those that impact more dysregulated genes and express similarly in the same tissue.https://doi.org/10.1186/s12920-019-0619-zDriver genesDysregulated expressionTissue-specific expressionHuman functional interaction networkVariation frequency |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Junrong Song Wei Peng Feng Wang Jianxin Wang |
spellingShingle |
Junrong Song Wei Peng Feng Wang Jianxin Wang Identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network BMC Medical Genomics Driver genes Dysregulated expression Tissue-specific expression Human functional interaction network Variation frequency |
author_facet |
Junrong Song Wei Peng Feng Wang Jianxin Wang |
author_sort |
Junrong Song |
title |
Identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network |
title_short |
Identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network |
title_full |
Identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network |
title_fullStr |
Identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network |
title_full_unstemmed |
Identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network |
title_sort |
identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network |
publisher |
BMC |
series |
BMC Medical Genomics |
issn |
1755-8794 |
publishDate |
2019-12-01 |
description |
Abstract Background Cancer as a kind of genomic alteration disease each year deprives many people’s life. The biggest challenge to overcome cancer is to identify driver genes that promote the cancer development from a huge amount of passenger mutations that have no effect on the selective growth advantage of cancer. In order to solve those problems, some researchers have started to focus on identification of driver genes by integrating networks with other biological information. However, more efforts should be needed to improve the prediction performance. Methods Considering the facts that driver genes have impact on expression of their downstream genes, they likely interact with each other to form functional modules and those modules should tend to be expressed similarly in the same tissue. We proposed a novel model named by DyTidriver to identify driver genes through involving the gene dysregulated expression, tissue-specific expression and variation frequency into the human functional interaction network (e.g. human FIN). Results This method was applied on 974 breast, 316 prostate and 230 lung cancer patients. The consequence shows our method outperformed other five existing methods in terms of Fscore, Precision and Recall values. The enrichment and cociter analysis illustrate DyTidriver can not only identifies the driver genes enriched in some significant pathways but also has the capability to figure out some unknown driver genes. Conclusion The final results imply that driver genes are those that impact more dysregulated genes and express similarly in the same tissue. |
topic |
Driver genes Dysregulated expression Tissue-specific expression Human functional interaction network Variation frequency |
url |
https://doi.org/10.1186/s12920-019-0619-z |
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