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Gene regulatory networks analysis of muscle-invasive bladder cancer subtypes using differential graphical model

BACKGROUND: Recently, erdafitinib (Balversa), the first targeted therapy drug for genetic alteration, was approved to metastatic urothelial carcinoma. Cancer genomics research has been greatly encouraged. Currently, a large number of gene regulatory networks between different states have been constr...

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Autores principales: Zhang, Yongqing, Chen, Qingyuan, Gong, Meiqin, Zeng, Yuanqi, Gao, Dongrui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8638098/
https://www.ncbi.nlm.nih.gov/pubmed/34852762
http://dx.doi.org/10.1186/s12864-021-08113-z
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author Zhang, Yongqing
Chen, Qingyuan
Gong, Meiqin
Zeng, Yuanqi
Gao, Dongrui
author_facet Zhang, Yongqing
Chen, Qingyuan
Gong, Meiqin
Zeng, Yuanqi
Gao, Dongrui
author_sort Zhang, Yongqing
collection PubMed
description BACKGROUND: Recently, erdafitinib (Balversa), the first targeted therapy drug for genetic alteration, was approved to metastatic urothelial carcinoma. Cancer genomics research has been greatly encouraged. Currently, a large number of gene regulatory networks between different states have been constructed, which can reveal the difference states of genes. However, they have not been applied to the subtypes of Muscle-invasive bladder cancer (MIBC). RESULTS: In this paper, we propose a method that construct gene regulatory networks under different molecular subtypes of MIBC, and analyse the regulatory differences between different molecular subtypes. Through differential expression analysis and the differential network analysis of the top 100 differential genes in the network, we find that SERPINI1, NOTUM, FGFR1 and other genes have significant differences in expression and regulatory relationship between MIBC subtypes. CONCLUSIONS: Furthermore, pathway enrichment analysis and differential network analysis demonstrate that Neuroactive ligand-receptor interaction and Cytokine-cytokine receptor interaction are significantly enriched pathways, and the genes contained in them are significant diversity in the subtypes of bladder cancer.
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spelling pubmed-86380982021-12-02 Gene regulatory networks analysis of muscle-invasive bladder cancer subtypes using differential graphical model Zhang, Yongqing Chen, Qingyuan Gong, Meiqin Zeng, Yuanqi Gao, Dongrui BMC Genomics Research BACKGROUND: Recently, erdafitinib (Balversa), the first targeted therapy drug for genetic alteration, was approved to metastatic urothelial carcinoma. Cancer genomics research has been greatly encouraged. Currently, a large number of gene regulatory networks between different states have been constructed, which can reveal the difference states of genes. However, they have not been applied to the subtypes of Muscle-invasive bladder cancer (MIBC). RESULTS: In this paper, we propose a method that construct gene regulatory networks under different molecular subtypes of MIBC, and analyse the regulatory differences between different molecular subtypes. Through differential expression analysis and the differential network analysis of the top 100 differential genes in the network, we find that SERPINI1, NOTUM, FGFR1 and other genes have significant differences in expression and regulatory relationship between MIBC subtypes. CONCLUSIONS: Furthermore, pathway enrichment analysis and differential network analysis demonstrate that Neuroactive ligand-receptor interaction and Cytokine-cytokine receptor interaction are significantly enriched pathways, and the genes contained in them are significant diversity in the subtypes of bladder cancer. BioMed Central 2021-12-01 /pmc/articles/PMC8638098/ /pubmed/34852762 http://dx.doi.org/10.1186/s12864-021-08113-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhang, Yongqing
Chen, Qingyuan
Gong, Meiqin
Zeng, Yuanqi
Gao, Dongrui
Gene regulatory networks analysis of muscle-invasive bladder cancer subtypes using differential graphical model
title Gene regulatory networks analysis of muscle-invasive bladder cancer subtypes using differential graphical model
title_full Gene regulatory networks analysis of muscle-invasive bladder cancer subtypes using differential graphical model
title_fullStr Gene regulatory networks analysis of muscle-invasive bladder cancer subtypes using differential graphical model
title_full_unstemmed Gene regulatory networks analysis of muscle-invasive bladder cancer subtypes using differential graphical model
title_short Gene regulatory networks analysis of muscle-invasive bladder cancer subtypes using differential graphical model
title_sort gene regulatory networks analysis of muscle-invasive bladder cancer subtypes using differential graphical model
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8638098/
https://www.ncbi.nlm.nih.gov/pubmed/34852762
http://dx.doi.org/10.1186/s12864-021-08113-z
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