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Updated: Jul 4, 2025
Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
Published on: August 1, 2018
Identification of new subtypes and potential genetic signatures in triple-negative breast cancer using weighted gene
1Department of Gland Surgery, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, China. fangdalang@stu.gxmu.edu.cn.
Objective:
Triple-negative breast cancer (TNBC) is a heterogeneous disease with aggressive behavior and poor prognosis. Here, we used gene expression profiling to define new subtypes of TNBC, which may improve prevention and treatment through personalized medicine.
Materials And Methods:
Gene expression profiles from the public datasets GSE76250, GSE61724, GSE61723, and GES76275 were subjected to co-expression analysis to identify differentially expressed genes (DEGs) between TNBC and non-TNBC tissues. Consistency clustering was used to define TNBC subtypes, whose correlation with gene modules was analyzed. Enrichment analysis was used to identify module genes' biological functions and pathways. Single-sample gene set enrichment analysis was used to assess immune cell infiltration in the different TNBC subtypes, and the ChAMP package was used to examine methylation sites in TNBC.
Results:
A total of 4,958 DEGs in TNBC were identified, which showed the same expression differences across all datasets as in the dataset GSE76250 and clustered into 9 co-expression modules. TNBC samples clustered into two subtypes based on nine hub genes from the modules. Class I showed the most significant correlation with module 1, whose genes were related mainly to interleukin-1 response, while class II showed the most significant correlation with module 6, whose genes were related mainly to the transforming growth factor-β pathway. Class I was significantly enriched in cell cycle and DNA replication, and tumors of this subtype showed lower immune cell infiltration than class II tumors. Tumor infiltration by Th2 cells correlated positively with the expression of MCM10 and negatively with the expression of PREX2. A greater methylation of CIDEC, DLC1, EDNRB, EGR2 and SRPK1 correlated with better prognosis.
Conclusions:
Class I TNBC, for which a useful biomarker is MCM10, may be associated with a worse prognosis than class II TNBC, for which PREX2 may serve as a biomarker.
