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Synthetic Lethality-based Identification of Targets for Anticancer Drugs in the Human Signaling Network
Chemotherapy agents can cause serious adverse effects by attacking both cancer tissues and normal tissues. Therefore, we proposed a synthetic lethality (SL) concept-based computational method to identify specific anticancer drug targets. First, a 3-step screening strategy (network-based, frequency-b...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5981615/ https://www.ncbi.nlm.nih.gov/pubmed/29855504 http://dx.doi.org/10.1038/s41598-018-26783-w |
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author | Liu, Lei Chen, Xiujie Hu, Chunyu Zhang, Denan Shao, Zhuo Jin, Qing Yang, Jingbo Xie, Hongbo Liu, Bo Hu, Ming Ke, Kehui |
author_facet | Liu, Lei Chen, Xiujie Hu, Chunyu Zhang, Denan Shao, Zhuo Jin, Qing Yang, Jingbo Xie, Hongbo Liu, Bo Hu, Ming Ke, Kehui |
author_sort | Liu, Lei |
collection | PubMed |
description | Chemotherapy agents can cause serious adverse effects by attacking both cancer tissues and normal tissues. Therefore, we proposed a synthetic lethality (SL) concept-based computational method to identify specific anticancer drug targets. First, a 3-step screening strategy (network-based, frequency-based and function-based screening) was proposed to identify the SL gene pairs by mining 697 cancer genes and the human signaling network, which had 6306 proteins and 62937 protein-protein interactions. The network-based screening was composed of a stability score constructed using a network information centrality measure (the average shortest path length) and the distance-based screening between the cancer gene and the non-cancer gene. Then, the non-cancer genes were extracted and annotated using drug-target interaction and drug description information to obtain potential anticancer drug targets. Finally, the human SL data in SynLethDB, the existing drug sensitivity data and text-mining were utilized for target validation. We successfully identified 2555 SL gene pairs and 57 potential anticancer drug targets. Among them, CDK1, CDK2, PLK1 and WEE1 were verified by all three aspects and could be preferentially used in specific targeted therapy in the future. |
format | Online Article Text |
id | pubmed-5981615 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-59816152018-06-07 Synthetic Lethality-based Identification of Targets for Anticancer Drugs in the Human Signaling Network Liu, Lei Chen, Xiujie Hu, Chunyu Zhang, Denan Shao, Zhuo Jin, Qing Yang, Jingbo Xie, Hongbo Liu, Bo Hu, Ming Ke, Kehui Sci Rep Article Chemotherapy agents can cause serious adverse effects by attacking both cancer tissues and normal tissues. Therefore, we proposed a synthetic lethality (SL) concept-based computational method to identify specific anticancer drug targets. First, a 3-step screening strategy (network-based, frequency-based and function-based screening) was proposed to identify the SL gene pairs by mining 697 cancer genes and the human signaling network, which had 6306 proteins and 62937 protein-protein interactions. The network-based screening was composed of a stability score constructed using a network information centrality measure (the average shortest path length) and the distance-based screening between the cancer gene and the non-cancer gene. Then, the non-cancer genes were extracted and annotated using drug-target interaction and drug description information to obtain potential anticancer drug targets. Finally, the human SL data in SynLethDB, the existing drug sensitivity data and text-mining were utilized for target validation. We successfully identified 2555 SL gene pairs and 57 potential anticancer drug targets. Among them, CDK1, CDK2, PLK1 and WEE1 were verified by all three aspects and could be preferentially used in specific targeted therapy in the future. Nature Publishing Group UK 2018-05-31 /pmc/articles/PMC5981615/ /pubmed/29855504 http://dx.doi.org/10.1038/s41598-018-26783-w Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Liu, Lei Chen, Xiujie Hu, Chunyu Zhang, Denan Shao, Zhuo Jin, Qing Yang, Jingbo Xie, Hongbo Liu, Bo Hu, Ming Ke, Kehui Synthetic Lethality-based Identification of Targets for Anticancer Drugs in the Human Signaling Network |
title | Synthetic Lethality-based Identification of Targets for Anticancer Drugs in the Human Signaling Network |
title_full | Synthetic Lethality-based Identification of Targets for Anticancer Drugs in the Human Signaling Network |
title_fullStr | Synthetic Lethality-based Identification of Targets for Anticancer Drugs in the Human Signaling Network |
title_full_unstemmed | Synthetic Lethality-based Identification of Targets for Anticancer Drugs in the Human Signaling Network |
title_short | Synthetic Lethality-based Identification of Targets for Anticancer Drugs in the Human Signaling Network |
title_sort | synthetic lethality-based identification of targets for anticancer drugs in the human signaling network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5981615/ https://www.ncbi.nlm.nih.gov/pubmed/29855504 http://dx.doi.org/10.1038/s41598-018-26783-w |
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