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Updated: Jan 23, 2026
Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model
Published on: March 20, 2020
Identification of differentially expressed genes between triple and non-triple-negative breast cancer using
Qixi Zhai1,2,3, Hao Li2,3, Liping Sun2,3
1Department of Ultrasound, the First Hospital of China Medical University, Shenyang, 110001, China.
Background:
Triple-negative breast cancer (TNBC), defined by lack of expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), is characterized by early recurrence of disease and poor survival.
Objective:
Here, we sought to identify genes associated with TNBC that could provide new insight into gene dysregulation in TNBC and, at the same time, provide additional potential therapeutic targets for breast cancer treatment.
Methods:
Gene expression profiles from accession series GSE76275 were downloaded from the Gene Expression Omnibus database (GEO). The Cancer Genome Atlas (TCGA) was used to validate potential hub genes in the TCGA database. Protein-protein interaction (PPI) networks were identified using STRING (Search Tool for the Retrieval of Interacting Genes/Proteins). Finally, overall survival (OS) and relapse-free survival (RFS) analysis of hub genes was performed using a Kaplan-Meier plotter online tool.
Results:
A total of 750 genes were identified after analysis of GSE76275. After validation with the TCGA database, a total of 155 differentially expressed genes (DEGs) were consistent with those identified by GSE76275. Based on the STRING database, we constructed a PPI network using the DEGs obtained from GSE76275 datasets. Furthermore, in the prognostic analysis of the 155 DEGs, we found that there were 10 genes associated with OS and 33 genes associated with RFS. Combined with the degree scores from the PPI network, a total of ten genes with the highest degree scores were selected as hub genes pertaining to TNBC.
Conclusion:
Our research provides new insight into the subnetwork of biomarkers connected with TNBC, which could be useful for prognostication and risk stratification of TNBC patients.
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