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Differentially expressed genes and key molecules of BRCA1/2-mutant breast cancer: evidence from bioinformatics analyses
BACKGROUND: BRCA1 and BRCA2 genes are currently proven to be closely related to high lifetime risks of breast cancer. To date, the closely related genes to BRCA1/2 mutations in breast cancer remains to be fully elucidated. This study aims to identify the gene expression profiles and interaction netw...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6979404/ https://www.ncbi.nlm.nih.gov/pubmed/31998560 http://dx.doi.org/10.7717/peerj.8403 |
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author | Li, Yue Zhou, Xiaoyan Liu, Jiali Yin, Yang Yuan, Xiaohong Yang, Ruihua Wang, Qi Ji, Jing He, Qian |
author_facet | Li, Yue Zhou, Xiaoyan Liu, Jiali Yin, Yang Yuan, Xiaohong Yang, Ruihua Wang, Qi Ji, Jing He, Qian |
author_sort | Li, Yue |
collection | PubMed |
description | BACKGROUND: BRCA1 and BRCA2 genes are currently proven to be closely related to high lifetime risks of breast cancer. To date, the closely related genes to BRCA1/2 mutations in breast cancer remains to be fully elucidated. This study aims to identify the gene expression profiles and interaction networks influenced by BRCA1/2 mutations, so as to reflect underlying disease mechanisms and provide new biomarkers for breast cancer diagnosis or prognosis. METHODS: Gene expression profiles from The Cancer Genome Atlas (TCGA) database were downloaded and combined with cBioPortal website to identify exact breast cancer patients with BRCA1/2 mutations. Gene set enrichment analysis (GSEA) was used to analyze some enriched pathways and biological processes associated BRCA mutations. For BRCA1/2-mutant breast cancer, wild-type breast cancer and corresponding normal tissues, three independent differentially expressed genes (DEGs) analysis were performed to validate potential hub genes with each other. Protein–protein interaction (PPI) networks, survival analysis and diagnostic value assessment helped identify key genes associated with BRCA1/2 mutations. RESULTS: The regulation process of cell cycle was significantly enriched in mutant group compared with wild-type group. A total of 294 genes were identified after analysis of DEGs between mutant patients and wild-type patients. Interestingly, by the other two comparisons, we identified 43 overlapping genes that not only significantly expressed in wild-type breast cancer patients relative to normal tissues, but more significantly expressed in BRCA1/2-mutant breast patients. Based on the STRING database and cytoscape software, we constructed a PPI network using 294 DEGs. Through topological analysis scores of the PPI network and 43 overlapping genes, we sought to select some genes, thereby using survival analysis and diagnostic value assessment to identify key genes pertaining to BRCA1/2-mutant breast cancer. CCNE1, NPBWR1, A2ML1, EXO1 and TTK displayed good prognostic/diagnostic value for breast cancer and BRCA1/2-mutant breast cancer. CONCLUSION: Our research provides comprehensive and new insights for the identification of biomarkers connected with BRCA mutations, availing diagnosis and treatment of breast cancer and BRCA1/2-mutant breast cancer patients. |
format | Online Article Text |
id | pubmed-6979404 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-69794042020-01-29 Differentially expressed genes and key molecules of BRCA1/2-mutant breast cancer: evidence from bioinformatics analyses Li, Yue Zhou, Xiaoyan Liu, Jiali Yin, Yang Yuan, Xiaohong Yang, Ruihua Wang, Qi Ji, Jing He, Qian PeerJ Bioinformatics BACKGROUND: BRCA1 and BRCA2 genes are currently proven to be closely related to high lifetime risks of breast cancer. To date, the closely related genes to BRCA1/2 mutations in breast cancer remains to be fully elucidated. This study aims to identify the gene expression profiles and interaction networks influenced by BRCA1/2 mutations, so as to reflect underlying disease mechanisms and provide new biomarkers for breast cancer diagnosis or prognosis. METHODS: Gene expression profiles from The Cancer Genome Atlas (TCGA) database were downloaded and combined with cBioPortal website to identify exact breast cancer patients with BRCA1/2 mutations. Gene set enrichment analysis (GSEA) was used to analyze some enriched pathways and biological processes associated BRCA mutations. For BRCA1/2-mutant breast cancer, wild-type breast cancer and corresponding normal tissues, three independent differentially expressed genes (DEGs) analysis were performed to validate potential hub genes with each other. Protein–protein interaction (PPI) networks, survival analysis and diagnostic value assessment helped identify key genes associated with BRCA1/2 mutations. RESULTS: The regulation process of cell cycle was significantly enriched in mutant group compared with wild-type group. A total of 294 genes were identified after analysis of DEGs between mutant patients and wild-type patients. Interestingly, by the other two comparisons, we identified 43 overlapping genes that not only significantly expressed in wild-type breast cancer patients relative to normal tissues, but more significantly expressed in BRCA1/2-mutant breast patients. Based on the STRING database and cytoscape software, we constructed a PPI network using 294 DEGs. Through topological analysis scores of the PPI network and 43 overlapping genes, we sought to select some genes, thereby using survival analysis and diagnostic value assessment to identify key genes pertaining to BRCA1/2-mutant breast cancer. CCNE1, NPBWR1, A2ML1, EXO1 and TTK displayed good prognostic/diagnostic value for breast cancer and BRCA1/2-mutant breast cancer. CONCLUSION: Our research provides comprehensive and new insights for the identification of biomarkers connected with BRCA mutations, availing diagnosis and treatment of breast cancer and BRCA1/2-mutant breast cancer patients. PeerJ Inc. 2020-01-21 /pmc/articles/PMC6979404/ /pubmed/31998560 http://dx.doi.org/10.7717/peerj.8403 Text en ©2020 Li et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Li, Yue Zhou, Xiaoyan Liu, Jiali Yin, Yang Yuan, Xiaohong Yang, Ruihua Wang, Qi Ji, Jing He, Qian Differentially expressed genes and key molecules of BRCA1/2-mutant breast cancer: evidence from bioinformatics analyses |
title | Differentially expressed genes and key molecules of BRCA1/2-mutant breast cancer: evidence from bioinformatics analyses |
title_full | Differentially expressed genes and key molecules of BRCA1/2-mutant breast cancer: evidence from bioinformatics analyses |
title_fullStr | Differentially expressed genes and key molecules of BRCA1/2-mutant breast cancer: evidence from bioinformatics analyses |
title_full_unstemmed | Differentially expressed genes and key molecules of BRCA1/2-mutant breast cancer: evidence from bioinformatics analyses |
title_short | Differentially expressed genes and key molecules of BRCA1/2-mutant breast cancer: evidence from bioinformatics analyses |
title_sort | differentially expressed genes and key molecules of brca1/2-mutant breast cancer: evidence from bioinformatics analyses |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6979404/ https://www.ncbi.nlm.nih.gov/pubmed/31998560 http://dx.doi.org/10.7717/peerj.8403 |
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