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Analysis of Breast Cancer Based on the Dysregulated Network

Breast cancer is a heterogeneous disease, and its development is closely associated with the underlying molecular regulatory network. In this paper, we propose a new way to measure the regulation strength between genes based on their expression values, and construct the dysregulated networks (DNs) f...

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Autores principales: Huo, Yanhao, Li, Xianbin, Xu, Peng, Bao, Zhenshen, Liu, Wenbin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8886234/
https://www.ncbi.nlm.nih.gov/pubmed/35242172
http://dx.doi.org/10.3389/fgene.2022.856075
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author Huo, Yanhao
Li, Xianbin
Xu, Peng
Bao, Zhenshen
Liu, Wenbin
author_facet Huo, Yanhao
Li, Xianbin
Xu, Peng
Bao, Zhenshen
Liu, Wenbin
author_sort Huo, Yanhao
collection PubMed
description Breast cancer is a heterogeneous disease, and its development is closely associated with the underlying molecular regulatory network. In this paper, we propose a new way to measure the regulation strength between genes based on their expression values, and construct the dysregulated networks (DNs) for the four subtypes of breast cancer. Our results show that the key dysregulated networks (KDNs) are significantly enriched in critical breast cancer-related pathways and driver genes; closely related to drug targets; and have significant differences in survival analysis. Moreover, the key dysregulated genes could serve as potential driver genes, drug targets, and prognostic markers for each breast cancer subtype. Therefore, the KDN is expected to be an effective and novel way to understand the mechanisms of breast cancer.
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spelling pubmed-88862342022-03-02 Analysis of Breast Cancer Based on the Dysregulated Network Huo, Yanhao Li, Xianbin Xu, Peng Bao, Zhenshen Liu, Wenbin Front Genet Genetics Breast cancer is a heterogeneous disease, and its development is closely associated with the underlying molecular regulatory network. In this paper, we propose a new way to measure the regulation strength between genes based on their expression values, and construct the dysregulated networks (DNs) for the four subtypes of breast cancer. Our results show that the key dysregulated networks (KDNs) are significantly enriched in critical breast cancer-related pathways and driver genes; closely related to drug targets; and have significant differences in survival analysis. Moreover, the key dysregulated genes could serve as potential driver genes, drug targets, and prognostic markers for each breast cancer subtype. Therefore, the KDN is expected to be an effective and novel way to understand the mechanisms of breast cancer. Frontiers Media S.A. 2022-02-15 /pmc/articles/PMC8886234/ /pubmed/35242172 http://dx.doi.org/10.3389/fgene.2022.856075 Text en Copyright © 2022 Huo, Li, Xu, Bao and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Huo, Yanhao
Li, Xianbin
Xu, Peng
Bao, Zhenshen
Liu, Wenbin
Analysis of Breast Cancer Based on the Dysregulated Network
title Analysis of Breast Cancer Based on the Dysregulated Network
title_full Analysis of Breast Cancer Based on the Dysregulated Network
title_fullStr Analysis of Breast Cancer Based on the Dysregulated Network
title_full_unstemmed Analysis of Breast Cancer Based on the Dysregulated Network
title_short Analysis of Breast Cancer Based on the Dysregulated Network
title_sort analysis of breast cancer based on the dysregulated network
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8886234/
https://www.ncbi.nlm.nih.gov/pubmed/35242172
http://dx.doi.org/10.3389/fgene.2022.856075
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