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A genomic and transcriptomic study toward breast cancer

Background: Breast carcinoma is well recognized to be having the highest global occurrence rate among all cancers, being the leading cause of cancer mortality in females. The aim of this study was to elucidate breast cancer at the genomic and transcriptomic levels in different subtypes so that we ca...

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Autores principales: Wang, Shan, Shang, Pei, Yao, Guangyu, Ye, Changsheng, Chen, Lujia, Hu, Xiaolei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9596791/
https://www.ncbi.nlm.nih.gov/pubmed/36313438
http://dx.doi.org/10.3389/fgene.2022.989565
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author Wang, Shan
Shang, Pei
Yao, Guangyu
Ye, Changsheng
Chen, Lujia
Hu, Xiaolei
author_facet Wang, Shan
Shang, Pei
Yao, Guangyu
Ye, Changsheng
Chen, Lujia
Hu, Xiaolei
author_sort Wang, Shan
collection PubMed
description Background: Breast carcinoma is well recognized to be having the highest global occurrence rate among all cancers, being the leading cause of cancer mortality in females. The aim of this study was to elucidate breast cancer at the genomic and transcriptomic levels in different subtypes so that we can develop more personalized treatments and precision medicine to obtain better outcomes. Method: In this study, an expression profiling dataset downloaded from the Gene Expression Omnibus database, GSE45827, was re-analyzed to compare the expression profiles of breast cancer samples in the different subtypes. Using the GEO2R tool, different expression genes were identified. Using the STRING online tool, the protein–protein interaction networks were conducted. Using the Cytoscape software, we found modules, seed genes, and hub genes and performed pathway enrichment analysis. The Kaplan–Meier plotter was used to analyze the overall survival. MicroRNAs and transcription factors targeted different expression genes and were predicted by the Enrichr web server. Result: The analysis of these elements implied that the carcinogenesis and development of triple-negative breast cancer were the most important and complicated in breast carcinoma, occupying the most different expression genes, modules, seed genes, hub genes, and the most complex protein–protein interaction network and signal pathway. In addition, the luminal A subtype might occur in a completely different way from the other three subtypes as the pathways enriched in the luminal A subtype did not overlap with the others. We identified 16 hub genes that were related to good prognosis in triple-negative breast cancer. Moreover, SRSF1 was negatively correlated with overall survival in the Her2 subtype, while in the luminal A subtype, it showed the opposite relationship. Also, in the luminal B subtype, CCNB1 and KIF23 were associated with poor prognosis. Furthermore, new transcription factors and microRNAs were introduced to breast cancer which would shed light upon breast cancer in a new way and provide a novel therapeutic strategy. Conclusion: We preliminarily delved into the potentially comprehensive molecular mechanisms of breast cancer by creating a holistic view at the genomic and transcriptomic levels in different subtypes using computational tools. We also introduced new prognosis-related genes and novel therapeutic strategies and cast new light upon breast cancer.
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spelling pubmed-95967912022-10-27 A genomic and transcriptomic study toward breast cancer Wang, Shan Shang, Pei Yao, Guangyu Ye, Changsheng Chen, Lujia Hu, Xiaolei Front Genet Genetics Background: Breast carcinoma is well recognized to be having the highest global occurrence rate among all cancers, being the leading cause of cancer mortality in females. The aim of this study was to elucidate breast cancer at the genomic and transcriptomic levels in different subtypes so that we can develop more personalized treatments and precision medicine to obtain better outcomes. Method: In this study, an expression profiling dataset downloaded from the Gene Expression Omnibus database, GSE45827, was re-analyzed to compare the expression profiles of breast cancer samples in the different subtypes. Using the GEO2R tool, different expression genes were identified. Using the STRING online tool, the protein–protein interaction networks were conducted. Using the Cytoscape software, we found modules, seed genes, and hub genes and performed pathway enrichment analysis. The Kaplan–Meier plotter was used to analyze the overall survival. MicroRNAs and transcription factors targeted different expression genes and were predicted by the Enrichr web server. Result: The analysis of these elements implied that the carcinogenesis and development of triple-negative breast cancer were the most important and complicated in breast carcinoma, occupying the most different expression genes, modules, seed genes, hub genes, and the most complex protein–protein interaction network and signal pathway. In addition, the luminal A subtype might occur in a completely different way from the other three subtypes as the pathways enriched in the luminal A subtype did not overlap with the others. We identified 16 hub genes that were related to good prognosis in triple-negative breast cancer. Moreover, SRSF1 was negatively correlated with overall survival in the Her2 subtype, while in the luminal A subtype, it showed the opposite relationship. Also, in the luminal B subtype, CCNB1 and KIF23 were associated with poor prognosis. Furthermore, new transcription factors and microRNAs were introduced to breast cancer which would shed light upon breast cancer in a new way and provide a novel therapeutic strategy. Conclusion: We preliminarily delved into the potentially comprehensive molecular mechanisms of breast cancer by creating a holistic view at the genomic and transcriptomic levels in different subtypes using computational tools. We also introduced new prognosis-related genes and novel therapeutic strategies and cast new light upon breast cancer. Frontiers Media S.A. 2022-10-12 /pmc/articles/PMC9596791/ /pubmed/36313438 http://dx.doi.org/10.3389/fgene.2022.989565 Text en Copyright © 2022 Wang, Shang, Yao, Ye, Chen and Hu. 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
Wang, Shan
Shang, Pei
Yao, Guangyu
Ye, Changsheng
Chen, Lujia
Hu, Xiaolei
A genomic and transcriptomic study toward breast cancer
title A genomic and transcriptomic study toward breast cancer
title_full A genomic and transcriptomic study toward breast cancer
title_fullStr A genomic and transcriptomic study toward breast cancer
title_full_unstemmed A genomic and transcriptomic study toward breast cancer
title_short A genomic and transcriptomic study toward breast cancer
title_sort genomic and transcriptomic study toward breast cancer
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9596791/
https://www.ncbi.nlm.nih.gov/pubmed/36313438
http://dx.doi.org/10.3389/fgene.2022.989565
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