Estrogen receptor 1 and progesterone receptor are distinct biomarkers and prognostic factors in estrogen receptor-positive breast cancer: Evidence from a bioinformatic analysis
Purpose: Few prognostic indicators involving differentially expressed genes (DEGs) between estrogen receptor (ER)-positive and ER-negative breast cancer (BC) have been reported. We aimed to screen important DEGs related to ER status and explore potential prognostic factors for patients with ER-posit...
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doaj-7cd6c66483f94c1291319d7e174760e22021-05-20T07:39:37ZengElsevierBiomedicine & Pharmacotherapy0753-33222020-01-01121109647Estrogen receptor 1 and progesterone receptor are distinct biomarkers and prognostic factors in estrogen receptor-positive breast cancer: Evidence from a bioinformatic analysisJun-Rong Wu0Yang Zhao1Xiao-Ping Zhou2Xue Qin3Department of Clinical Laboratory, The Affiliated Tumor Hospital of Guangxi Medical University, No. 71 Embankment Road, Nanning, 530021, Guangxi, ChinaDepartment of Radiology, The Affiliated Tumor Hospital of Guangxi Medical University, No. 71 Embankment Road, Nanning, 530021, Guangxi, ChinaDepartment of Clinical Laboratory, The First Affiliated Hospital of Guangxi University of Chinese Medicine, No. 89-9 Dongge Road, Nanning, 530023, Guangxi, ChinaDepartment of Clinical Laboratory, The Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, 530021, Guangxi, China; Corresponding author.Purpose: Few prognostic indicators involving differentially expressed genes (DEGs) between estrogen receptor (ER)-positive and ER-negative breast cancer (BC) have been reported. We aimed to screen important DEGs related to ER status and explore potential prognostic factors for patients with ER-positive BC. Materials and methods: Two microarray datasets (GSE22093 and GSE23988) downloaded from the Gene Expression Omnibus database were analyzed to identify DEGs between ER-positive and ER-negative BC tissue. Functional enrichment analysis of DEGs was performed using the Database for Annotation, Visualization, and Integrated Discovery server. Protein-protein interactions of the DEGs were analyzed using the Search Tool for the Retrieval of Interacting Genes. Subsequently, we studied the expression of hub genes in different histological types of BC using the Oncomine database. The online Kaplan-Meier (K-M) plotter survival analysis tool was utilized to evaluate the prognostic value of the expression of hub genes in ER-positive BC patients. Based on the results of K-M plotter analysis, we investigated the expression profiles of significant hub genes in an array of cancer cell lines using the Cancer Cell Line Encyclopedia database. Results: A total of 194 DEGs were identified, comprising 141 upregulated and 53 downregulated genes. GO analysis revealed that the DEGs were mainly enriched in the extracellular exosome of the cellular component category. Ten hub genes were upregulated in ER-positive BC, and overexpression of estrogen receptor 1 (ESR1) mRNA was correlated with worse relapse-free survival (RFS). In contrast, overexpressed mRNA of progesterone receptor (PGR) was associated with longer RFS in patients with ER-positive BC. The expression of ESR1 was associated with GATA3, whereas that of PGR was correlated with ABLIM3. Both ESR1 and PGR were highly expressed in BC cell lines. Conclusions: The results suggest that mRNA expression levels of ESR1 and PGR can be considered distinct biomarkers and essential prognostic factors for ER-positive BC.http://www.sciencedirect.com/science/article/pii/S0753332219352692Estrogen receptorBreast cancerHub genesExpression profiling data |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jun-Rong Wu Yang Zhao Xiao-Ping Zhou Xue Qin |
spellingShingle |
Jun-Rong Wu Yang Zhao Xiao-Ping Zhou Xue Qin Estrogen receptor 1 and progesterone receptor are distinct biomarkers and prognostic factors in estrogen receptor-positive breast cancer: Evidence from a bioinformatic analysis Biomedicine & Pharmacotherapy Estrogen receptor Breast cancer Hub genes Expression profiling data |
author_facet |
Jun-Rong Wu Yang Zhao Xiao-Ping Zhou Xue Qin |
author_sort |
Jun-Rong Wu |
title |
Estrogen receptor 1 and progesterone receptor are distinct biomarkers and prognostic factors in estrogen receptor-positive breast cancer: Evidence from a bioinformatic analysis |
title_short |
Estrogen receptor 1 and progesterone receptor are distinct biomarkers and prognostic factors in estrogen receptor-positive breast cancer: Evidence from a bioinformatic analysis |
title_full |
Estrogen receptor 1 and progesterone receptor are distinct biomarkers and prognostic factors in estrogen receptor-positive breast cancer: Evidence from a bioinformatic analysis |
title_fullStr |
Estrogen receptor 1 and progesterone receptor are distinct biomarkers and prognostic factors in estrogen receptor-positive breast cancer: Evidence from a bioinformatic analysis |
title_full_unstemmed |
Estrogen receptor 1 and progesterone receptor are distinct biomarkers and prognostic factors in estrogen receptor-positive breast cancer: Evidence from a bioinformatic analysis |
title_sort |
estrogen receptor 1 and progesterone receptor are distinct biomarkers and prognostic factors in estrogen receptor-positive breast cancer: evidence from a bioinformatic analysis |
publisher |
Elsevier |
series |
Biomedicine & Pharmacotherapy |
issn |
0753-3322 |
publishDate |
2020-01-01 |
description |
Purpose: Few prognostic indicators involving differentially expressed genes (DEGs) between estrogen receptor (ER)-positive and ER-negative breast cancer (BC) have been reported. We aimed to screen important DEGs related to ER status and explore potential prognostic factors for patients with ER-positive BC. Materials and methods: Two microarray datasets (GSE22093 and GSE23988) downloaded from the Gene Expression Omnibus database were analyzed to identify DEGs between ER-positive and ER-negative BC tissue. Functional enrichment analysis of DEGs was performed using the Database for Annotation, Visualization, and Integrated Discovery server. Protein-protein interactions of the DEGs were analyzed using the Search Tool for the Retrieval of Interacting Genes. Subsequently, we studied the expression of hub genes in different histological types of BC using the Oncomine database. The online Kaplan-Meier (K-M) plotter survival analysis tool was utilized to evaluate the prognostic value of the expression of hub genes in ER-positive BC patients. Based on the results of K-M plotter analysis, we investigated the expression profiles of significant hub genes in an array of cancer cell lines using the Cancer Cell Line Encyclopedia database. Results: A total of 194 DEGs were identified, comprising 141 upregulated and 53 downregulated genes. GO analysis revealed that the DEGs were mainly enriched in the extracellular exosome of the cellular component category. Ten hub genes were upregulated in ER-positive BC, and overexpression of estrogen receptor 1 (ESR1) mRNA was correlated with worse relapse-free survival (RFS). In contrast, overexpressed mRNA of progesterone receptor (PGR) was associated with longer RFS in patients with ER-positive BC. The expression of ESR1 was associated with GATA3, whereas that of PGR was correlated with ABLIM3. Both ESR1 and PGR were highly expressed in BC cell lines. Conclusions: The results suggest that mRNA expression levels of ESR1 and PGR can be considered distinct biomarkers and essential prognostic factors for ER-positive BC. |
topic |
Estrogen receptor Breast cancer Hub genes Expression profiling data |
url |
http://www.sciencedirect.com/science/article/pii/S0753332219352692 |
work_keys_str_mv |
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