Abstract

Gene expression profiling can predict the clinical outcome of breast cancer. However, further classification is needed to improve the prediction of prognosis. For further classification, bioinformatics analysis using The Cancer Genome Atlas (TCGA) dataset was performed to determine the effect of the upregulation of basal-like breast cancer-related genes in the PAM50 dataset on survival. Survival-related genes were further evaluated in a large cohort in the Kaplan-Meier plotter (KMplot) dataset. These analyses revealed that increased expression of EXO1, a basal-like breast cancer-related gene, was linked to longer overall survival in basal-like breast cancer [hazard ratio (HR) = 0.135, p < 0.0001 in TCGA and HR = 0.479, p = 0.036 in KMplot] and remained significant in multivariate analysis. Therefore, we propose that basal-like breast cancer can be further divided into two groups with different prognoses based on EXO1 expression.