Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks

Abstract Each cancer type has its own molecular signaling network. Analyzing the dynamics of molecular signaling networks can provide useful information for identifying drug target genes. In the present study, we consider an on-network dynamics model—the outside competitive dynamics model—wherein an...

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Autores principales:	Tien-Dzung Tran, Duc-Tinh Pham
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ce96ad22568145018b388b22d94a277a
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spelling	oai:doaj.org-article:ce96ad22568145018b388b22d94a277a2021-12-02T16:14:56ZIdentification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks10.1038/s41598-021-93336-z2045-2322https://doaj.org/article/ce96ad22568145018b388b22d94a277a2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93336-zhttps://doaj.org/toc/2045-2322Abstract Each cancer type has its own molecular signaling network. Analyzing the dynamics of molecular signaling networks can provide useful information for identifying drug target genes. In the present study, we consider an on-network dynamics model—the outside competitive dynamics model—wherein an inside leader and an opponent competitor outside the system have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. If any normal agent links to the external competitor, the state of each normal agent will converge to a stable value, indicating support to the leader against the impact of the competitor. We determined the total support of normal agents to each leader in various networks and observed that the total support correlates with hierarchical closeness, which identifies biomarker genes in a cancer signaling network. Of note, by experimenting on 17 cancer signaling networks from the KEGG database, we observed that 82% of the genes among the top 3 agents with the highest total support are anticancer drug target genes. This result outperforms those of four previous prediction methods of common cancer drug targets. Our study indicates that driver agents with high support from the other agents against the impact of the external opponent agent are most likely to be anticancer drug target genes.Tien-Dzung TranDuc-Tinh PhamNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tien-Dzung Tran Duc-Tinh Pham Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks
description	Abstract Each cancer type has its own molecular signaling network. Analyzing the dynamics of molecular signaling networks can provide useful information for identifying drug target genes. In the present study, we consider an on-network dynamics model—the outside competitive dynamics model—wherein an inside leader and an opponent competitor outside the system have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. If any normal agent links to the external competitor, the state of each normal agent will converge to a stable value, indicating support to the leader against the impact of the competitor. We determined the total support of normal agents to each leader in various networks and observed that the total support correlates with hierarchical closeness, which identifies biomarker genes in a cancer signaling network. Of note, by experimenting on 17 cancer signaling networks from the KEGG database, we observed that 82% of the genes among the top 3 agents with the highest total support are anticancer drug target genes. This result outperforms those of four previous prediction methods of common cancer drug targets. Our study indicates that driver agents with high support from the other agents against the impact of the external opponent agent are most likely to be anticancer drug target genes.
format	article
author	Tien-Dzung Tran Duc-Tinh Pham
author_facet	Tien-Dzung Tran Duc-Tinh Pham
author_sort	Tien-Dzung Tran
title	Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks
title_short	Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks
title_full	Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks
title_fullStr	Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks
title_full_unstemmed	Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks
title_sort	identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ce96ad22568145018b388b22d94a277a
work_keys_str_mv	AT tiendzungtran identificationofanticancerdrugtargetgenesusinganoutsidecompetitivedynamicsmodeloncancersignalingnetworks AT ductinhpham identificationofanticancerdrugtargetgenesusinganoutsidecompetitivedynamicsmodeloncancersignalingnetworks
_version_	1718384319412568064

Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks

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