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Identification of key genes unique to the luminal a and basal-like breast cancer subtypes via bioinformatic analysis

BACKGROUND: Breast cancer subtypes are statistically associated with prognosis. The search for markers of breast tumor heterogeneity and the development of precision medicine for patients are the current focuses of the field. METHODS: We used a bioinformatic approach to identify key disease-causing...

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Autores principales: Jia, Rong, Li, Zhongxian, Liang, Wei, Ji, Yucheng, Weng, Yujie, Liang, Ying, Ning, Pengfei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7568373/
https://www.ncbi.nlm.nih.gov/pubmed/33066779
http://dx.doi.org/10.1186/s12957-020-02042-z
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author Jia, Rong
Li, Zhongxian
Liang, Wei
Ji, Yucheng
Weng, Yujie
Liang, Ying
Ning, Pengfei
author_facet Jia, Rong
Li, Zhongxian
Liang, Wei
Ji, Yucheng
Weng, Yujie
Liang, Ying
Ning, Pengfei
author_sort Jia, Rong
collection PubMed
description BACKGROUND: Breast cancer subtypes are statistically associated with prognosis. The search for markers of breast tumor heterogeneity and the development of precision medicine for patients are the current focuses of the field. METHODS: We used a bioinformatic approach to identify key disease-causing genes unique to the luminal A and basal-like subtypes of breast cancer. First, we retrieved gene expression data for luminal A breast cancer, basal-like breast cancer, and normal breast tissue samples from The Cancer Genome Atlas database. The differentially expressed genes unique to the 2 breast cancer subtypes were identified and subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We constructed protein–protein interaction networks of the differentially expressed genes. Finally, we analyzed the key modules of the networks, which we combined with survival data to identify the unique cancer genes associated with each breast cancer subtype. RESULTS: We identified 1114 differentially expressed genes in luminal A breast cancer and 1042 differentially expressed genes in basal-like breast cancer, of which the subtypes shared 500. We observed 614 and 542 differentially expressed genes unique to luminal A and basal-like breast cancer, respectively. Through enrichment analyses, protein–protein interaction network analysis, and module mining, we identified 8 key differentially expressed genes unique to each subtype. Analysis of the gene expression data in the context of the survival data revealed that high expression of NMUR1 and NCAM1 in luminal A breast cancer statistically correlated with poor prognosis, whereas the low expression levels of CDC7, KIF18A, STIL, and CKS2 in basal-like breast cancer statistically correlated with poor prognosis. CONCLUSIONS: NMUR1 and NCAM1 are novel key disease-causing genes for luminal A breast cancer, and STIL is a novel key disease-causing gene for basal-like breast cancer. These genes are potential targets for clinical treatment.
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spelling pubmed-75683732020-10-20 Identification of key genes unique to the luminal a and basal-like breast cancer subtypes via bioinformatic analysis Jia, Rong Li, Zhongxian Liang, Wei Ji, Yucheng Weng, Yujie Liang, Ying Ning, Pengfei World J Surg Oncol Research BACKGROUND: Breast cancer subtypes are statistically associated with prognosis. The search for markers of breast tumor heterogeneity and the development of precision medicine for patients are the current focuses of the field. METHODS: We used a bioinformatic approach to identify key disease-causing genes unique to the luminal A and basal-like subtypes of breast cancer. First, we retrieved gene expression data for luminal A breast cancer, basal-like breast cancer, and normal breast tissue samples from The Cancer Genome Atlas database. The differentially expressed genes unique to the 2 breast cancer subtypes were identified and subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We constructed protein–protein interaction networks of the differentially expressed genes. Finally, we analyzed the key modules of the networks, which we combined with survival data to identify the unique cancer genes associated with each breast cancer subtype. RESULTS: We identified 1114 differentially expressed genes in luminal A breast cancer and 1042 differentially expressed genes in basal-like breast cancer, of which the subtypes shared 500. We observed 614 and 542 differentially expressed genes unique to luminal A and basal-like breast cancer, respectively. Through enrichment analyses, protein–protein interaction network analysis, and module mining, we identified 8 key differentially expressed genes unique to each subtype. Analysis of the gene expression data in the context of the survival data revealed that high expression of NMUR1 and NCAM1 in luminal A breast cancer statistically correlated with poor prognosis, whereas the low expression levels of CDC7, KIF18A, STIL, and CKS2 in basal-like breast cancer statistically correlated with poor prognosis. CONCLUSIONS: NMUR1 and NCAM1 are novel key disease-causing genes for luminal A breast cancer, and STIL is a novel key disease-causing gene for basal-like breast cancer. These genes are potential targets for clinical treatment. BioMed Central 2020-10-16 /pmc/articles/PMC7568373/ /pubmed/33066779 http://dx.doi.org/10.1186/s12957-020-02042-z 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
Jia, Rong
Li, Zhongxian
Liang, Wei
Ji, Yucheng
Weng, Yujie
Liang, Ying
Ning, Pengfei
Identification of key genes unique to the luminal a and basal-like breast cancer subtypes via bioinformatic analysis
title Identification of key genes unique to the luminal a and basal-like breast cancer subtypes via bioinformatic analysis
title_full Identification of key genes unique to the luminal a and basal-like breast cancer subtypes via bioinformatic analysis
title_fullStr Identification of key genes unique to the luminal a and basal-like breast cancer subtypes via bioinformatic analysis
title_full_unstemmed Identification of key genes unique to the luminal a and basal-like breast cancer subtypes via bioinformatic analysis
title_short Identification of key genes unique to the luminal a and basal-like breast cancer subtypes via bioinformatic analysis
title_sort identification of key genes unique to the luminal a and basal-like breast cancer subtypes via bioinformatic analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7568373/
https://www.ncbi.nlm.nih.gov/pubmed/33066779
http://dx.doi.org/10.1186/s12957-020-02042-z
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