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Updated: Feb 3, 2026

Building Up a High-throughput Screening Platform to Assess the Heterogeneity of HER2 Gene Amplification in Breast Cancers
Published on: December 5, 2017
Identification of key candidate genes, pathways and related prognostic values in ER-negative/HER2-negative breast
Nan Shao1, Kaitao Yuan, Yunjian Zhang
1Breast Disease Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, China.
Purpose:
Breast cancer possesses different molecular expressions and biological behaviors. The purpose of this study was to identify the key genes, pathways, and related prognostic values in estrogen receptor (ER)-negative/human epidermal growth factor 2 (HER2)-negative breast cancer by bioinformatics analysis.
Methods:
The mRNA expression profiles of GSE20194 and GSE23988 were obtained from the Gene Expression Omnibus (GEO) database. Differently expressed genes (DEGs) were analyzed by GEO2R. A functional and pathway enrichment analysis of DEGs was conducted using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and a module analysis of the PPI network was conducted using Cytoscape software. Survival analysis of hub genes was analyzed using the Kaplan-Meier plotter online tool.
Results:
108 ER-negative/HER2-negative and 172 ER-positive/HER2-negative breast cancer samples were collected from the datasets GSE20194 and GSE23988. A total of 355 DEGs were identified in the ER-negative/HER2-negative samples, including 140 up-regulated and 215 down-regulated genes. The PPI network of DEGs consisted of 265 nodes and 648 edges. A significant module (12 nodes and 56 edges) was acquired from the PPI network of DEGs. Geneontology (GO) and pathway enrichment analysis demonstrated that this module was mainly related with transcription, cell proliferation, binding, and pathways in the PI3K-Akt signaling pathway. The high expression of CCNE1, KRT16, and MYBL2 was associated with worse relapse-free survival (RFS) and overall survival (OS) in ER-negative/HER2-negative breast cancer.
Conclusions:
An integrated bioinformatics analysis was utilized to discover key candidate genes and pathways in ER-negative/HER2-negative breast cancer. This can improve the understanding of molecular mechanisms and provide potential candidate genes for diagnosis, prognosis, and individualized therapy.
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