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The Analysis of Relevant Gene Networks Based on Driver Genes in Breast Cancer

Background: The occurrence and development of breast cancer has a strong correlation with a person’s genetics. Therefore, it is important to analyze the genetic factors of breast cancer for future development of potential targeted therapies from the genetic level. Methods: In this study, we complete...

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Autores principales: Qu, Luxuan, Wang, Zhiqiong, Zhang, Hao, Wang, Zhongyang, Liu, Caigang, Qian, Wei, Xin, Junchang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689550/
https://www.ncbi.nlm.nih.gov/pubmed/36428940
http://dx.doi.org/10.3390/diagnostics12112882
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author Qu, Luxuan
Wang, Zhiqiong
Zhang, Hao
Wang, Zhongyang
Liu, Caigang
Qian, Wei
Xin, Junchang
author_facet Qu, Luxuan
Wang, Zhiqiong
Zhang, Hao
Wang, Zhongyang
Liu, Caigang
Qian, Wei
Xin, Junchang
author_sort Qu, Luxuan
collection PubMed
description Background: The occurrence and development of breast cancer has a strong correlation with a person’s genetics. Therefore, it is important to analyze the genetic factors of breast cancer for future development of potential targeted therapies from the genetic level. Methods: In this study, we complete an analysis of the relevant protein–protein interaction network relating to breast cancer. This includes three steps, which are breast cancer-relevant genes selection using mutual information method, protein–protein interaction network reconstruction based on the STRING database, and vital genes calculating by nodes centrality analysis. Results: The 230 breast cancer-relevant genes were chosen in gene selection to reconstruct the protein–protein interaction network and some vital genes were calculated by node centrality analyses. Node centrality analyses conducted with the top 10 and top 20 values of each metric found 19 and 39 statistically vital genes, respectively. In order to prove the biological significance of these vital genes, we carried out the survival analysis and DNA methylation analysis, inquired about the prognosis in other cancer tissues and the RNA expression level in breast cancer. The results all proved the validity of the selected genes. Conclusions: These genes could provide a valuable reference in clinical treatment among breast cancer patients.
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spelling pubmed-96895502022-11-25 The Analysis of Relevant Gene Networks Based on Driver Genes in Breast Cancer Qu, Luxuan Wang, Zhiqiong Zhang, Hao Wang, Zhongyang Liu, Caigang Qian, Wei Xin, Junchang Diagnostics (Basel) Article Background: The occurrence and development of breast cancer has a strong correlation with a person’s genetics. Therefore, it is important to analyze the genetic factors of breast cancer for future development of potential targeted therapies from the genetic level. Methods: In this study, we complete an analysis of the relevant protein–protein interaction network relating to breast cancer. This includes three steps, which are breast cancer-relevant genes selection using mutual information method, protein–protein interaction network reconstruction based on the STRING database, and vital genes calculating by nodes centrality analysis. Results: The 230 breast cancer-relevant genes were chosen in gene selection to reconstruct the protein–protein interaction network and some vital genes were calculated by node centrality analyses. Node centrality analyses conducted with the top 10 and top 20 values of each metric found 19 and 39 statistically vital genes, respectively. In order to prove the biological significance of these vital genes, we carried out the survival analysis and DNA methylation analysis, inquired about the prognosis in other cancer tissues and the RNA expression level in breast cancer. The results all proved the validity of the selected genes. Conclusions: These genes could provide a valuable reference in clinical treatment among breast cancer patients. MDPI 2022-11-21 /pmc/articles/PMC9689550/ /pubmed/36428940 http://dx.doi.org/10.3390/diagnostics12112882 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Qu, Luxuan
Wang, Zhiqiong
Zhang, Hao
Wang, Zhongyang
Liu, Caigang
Qian, Wei
Xin, Junchang
The Analysis of Relevant Gene Networks Based on Driver Genes in Breast Cancer
title The Analysis of Relevant Gene Networks Based on Driver Genes in Breast Cancer
title_full The Analysis of Relevant Gene Networks Based on Driver Genes in Breast Cancer
title_fullStr The Analysis of Relevant Gene Networks Based on Driver Genes in Breast Cancer
title_full_unstemmed The Analysis of Relevant Gene Networks Based on Driver Genes in Breast Cancer
title_short The Analysis of Relevant Gene Networks Based on Driver Genes in Breast Cancer
title_sort analysis of relevant gene networks based on driver genes in breast cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689550/
https://www.ncbi.nlm.nih.gov/pubmed/36428940
http://dx.doi.org/10.3390/diagnostics12112882
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