Dataset Information

Identification of key differentially expressed genes between ER-positive/HER2-negative breast cancer and ER-negative/HER2-negative breast cancer using integrated bioinformatics analysis.

ABSTRACT: Background:Treatment strategies for various subtypes of breast cancer (BC) are different based on their distinct molecular characteristics. Therefore, it is very important to identify key differentially expressed genes (DEGs) between ER-positive/HER2-negative BC and ER-negative/HER2-negative BC. Methods:Gene expression profiles of GSE22093 and GSE23988 were obtained from the Gene Expression Omnibus database. There were 74 ER-positive/HER2-negative BC tissues and 85 ER-negative/HER2-negative BC tissues in the two profile datasets. DEGs between ER-positive/HER2-negative tissues and ER-negative/HER2-negative BC tissues were identified by the GEO2R tool. The common DEGs among the two datasets were detected with Venn software online. Next, we made use of the Database for Annotation, Visualization and Integrated Discovery to analyze enriched Kyoto Encyclopedia of Gene and Genome (KEGG) pathways and gene ontology terms. Then, the protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with the Search Tool for the Retrieval of Interacting Genes. Of the proteins in the PPI network, Molecular Complex Detection plug-in analysis identified nine core upregulated genes and one core downregulated gene. UALCAN and Gene Expression Profiling Interactive Analysis were applied to determine the expression of these 10 genes in BC. Furthermore, for the analysis of overall survival among those genes, the Kaplan-Meier method was implemented. Results:Ninety-three common DEGs (63 upregulated and 30 downregulated) were identified. KEGG pathway enrichment analysis showed that upregulated DEGs were particularly enriched in the progesterone-mediated oocyte maturation pathway. In addition, PGR might be a prognostic biomarker for ER-positive/HER2-negative BC. CCND1 is a poor prognostic biomarker for ER-positive/HER2-negative BC and ER-negative/HER2-negative BC. Moreover, TFF1, AGR2 and EGFR might be predictive biomarkers of node metastasis in ER-positive/HER2-negative BC and ER-negative/HER2-negative BC. Conclusions:CCND1, AGR2, PGR, TFF1 and EGFR are the key DEGs between ER-positive/HER2-negative BC and ER-negative/HER2-negative BC. Further studies are required to confirm the functions of the identified genes.

SUBMITTER: Gan S

PROVIDER: S-EPMC7347802 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Identification of key differentially expressed genes between ER-positive/HER2-negative breast cancer and ER-negative/HER2-negative breast cancer using integrated bioinformatics analysis.

Gan Siyuan S Dai Haixia H Li Rujia R Liu Wang W Ye Ruifang R Ha Yanping Y Di Xiaoqing X Hu Wenhua W Zhang Zhi Z Sun Yanqin Y

Gland surgery 20200601 3

<h4>Background</h4>Treatment strategies for various subtypes of breast cancer (BC) are different based on their distinct molecular characteristics. Therefore, it is very important to identify key differentially expressed genes (DEGs) between ER-positive/HER2-negative BC and ER-negative/HER2-negative BC.<h4>Methods</h4>Gene expression profiles of GSE22093 and GSE23988 were obtained from the Gene Expression Omnibus database. There were 74 ER-positive/HER2-negative BC tissues and 85 ER-negative/HER ...[more]

PMID: 32775256

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Dataset Information

Identification of key differentially expressed genes between ER-positive/HER2-negative breast cancer and ER-negative/HER2-negative breast cancer using integrated bioinformatics analysis.

Publications

Identification of key differentially expressed genes between ER-positive/HER2-negative breast cancer and ER-negative/HER2-negative breast cancer using integrated bioinformatics analysis.

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