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Identification of key genes controlling breast cancer stem cell characteristics via stemness indices analysis
BACKGROUND: With the gradual unveiling of tumour heterogeneity, cancer stem cells (CSCs) are now being considered the initial component of tumour initiation. However, the mechanisms of the growth and maintenance of breast cancer (BRCA) stem cells are still unknown. METHODS: To explore the crucial ge...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7014665/ https://www.ncbi.nlm.nih.gov/pubmed/32050983 http://dx.doi.org/10.1186/s12967-020-02260-9 |
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author | Pei, Jianying Wang, Yanxia Li, Yan |
author_facet | Pei, Jianying Wang, Yanxia Li, Yan |
author_sort | Pei, Jianying |
collection | PubMed |
description | BACKGROUND: With the gradual unveiling of tumour heterogeneity, cancer stem cells (CSCs) are now being considered the initial component of tumour initiation. However, the mechanisms of the growth and maintenance of breast cancer (BRCA) stem cells are still unknown. METHODS: To explore the crucial genes modulating BRCA stemness characteristics, we combined the gene expression value and mRNA expression-based stemness index (mRNAsi) of samples from The Cancer Genome Atlas (TCGA), and the mRNAsi was corrected using the tumour purity (corrected mRNAsi). mRNAsi and corrected mRNAsi were analysed and showed a close relationship with BRCA clinical characteristics, including tumour depth, pathological staging and survival status. Next, weighted gene co-expression network analysis (WGCNA) was applied to distinguish crucial gene modules and key genes. A series of functional analyses and expression validation of key genes were conducted using multiple databases, including Oncomine, Gene Expression Omnibus (GEO) and Gene Expression Profiling Integrative Analysis (GEPIA). RESULTS: This study found that mRNAsi and corrected mRNAsi scores were higher in BRCA tissues than that in normal tissues, and both of them increased with tumour stage. Higher corrected mRNAsi scores showed worse overall survival outcomes. We screened 3 modules and 32 key genes, and those key genes were found to be strongly correlated with each other. Functional analysis revealed that the key genes were related to cell fate decision events such as the cell cycle, cellular senescence, chromosome segregation and mitotic nuclear division. Among 32 key genes, we identified 12 genes that strongly correlated with BRCA survival. CONCLUSIONS: Thirty-two genes were found to be closely related to BRCA stem cell characteristics; among them, 12 genes showed prognosis-oriented effects in BRCA patients. The most significant signalling pathway related to stemness in BRCA was the cell cycle pathway, which may support new ideas for screening therapeutic targets to inhibit BRCA stem characteristics. These findings may highlight some therapeutic targets for inhibiting BRCA stem cells. |
format | Online Article Text |
id | pubmed-7014665 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-70146652020-02-18 Identification of key genes controlling breast cancer stem cell characteristics via stemness indices analysis Pei, Jianying Wang, Yanxia Li, Yan J Transl Med Research BACKGROUND: With the gradual unveiling of tumour heterogeneity, cancer stem cells (CSCs) are now being considered the initial component of tumour initiation. However, the mechanisms of the growth and maintenance of breast cancer (BRCA) stem cells are still unknown. METHODS: To explore the crucial genes modulating BRCA stemness characteristics, we combined the gene expression value and mRNA expression-based stemness index (mRNAsi) of samples from The Cancer Genome Atlas (TCGA), and the mRNAsi was corrected using the tumour purity (corrected mRNAsi). mRNAsi and corrected mRNAsi were analysed and showed a close relationship with BRCA clinical characteristics, including tumour depth, pathological staging and survival status. Next, weighted gene co-expression network analysis (WGCNA) was applied to distinguish crucial gene modules and key genes. A series of functional analyses and expression validation of key genes were conducted using multiple databases, including Oncomine, Gene Expression Omnibus (GEO) and Gene Expression Profiling Integrative Analysis (GEPIA). RESULTS: This study found that mRNAsi and corrected mRNAsi scores were higher in BRCA tissues than that in normal tissues, and both of them increased with tumour stage. Higher corrected mRNAsi scores showed worse overall survival outcomes. We screened 3 modules and 32 key genes, and those key genes were found to be strongly correlated with each other. Functional analysis revealed that the key genes were related to cell fate decision events such as the cell cycle, cellular senescence, chromosome segregation and mitotic nuclear division. Among 32 key genes, we identified 12 genes that strongly correlated with BRCA survival. CONCLUSIONS: Thirty-two genes were found to be closely related to BRCA stem cell characteristics; among them, 12 genes showed prognosis-oriented effects in BRCA patients. The most significant signalling pathway related to stemness in BRCA was the cell cycle pathway, which may support new ideas for screening therapeutic targets to inhibit BRCA stem characteristics. These findings may highlight some therapeutic targets for inhibiting BRCA stem cells. BioMed Central 2020-02-12 /pmc/articles/PMC7014665/ /pubmed/32050983 http://dx.doi.org/10.1186/s12967-020-02260-9 Text en © The Author(s) 2020 Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver (http://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 Pei, Jianying Wang, Yanxia Li, Yan Identification of key genes controlling breast cancer stem cell characteristics via stemness indices analysis |
title | Identification of key genes controlling breast cancer stem cell characteristics via stemness indices analysis |
title_full | Identification of key genes controlling breast cancer stem cell characteristics via stemness indices analysis |
title_fullStr | Identification of key genes controlling breast cancer stem cell characteristics via stemness indices analysis |
title_full_unstemmed | Identification of key genes controlling breast cancer stem cell characteristics via stemness indices analysis |
title_short | Identification of key genes controlling breast cancer stem cell characteristics via stemness indices analysis |
title_sort | identification of key genes controlling breast cancer stem cell characteristics via stemness indices analysis |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7014665/ https://www.ncbi.nlm.nih.gov/pubmed/32050983 http://dx.doi.org/10.1186/s12967-020-02260-9 |
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