Breast cancer is one of the most leading causes of death in females all over the world. Although, the incidence rate of breast cancer is low, the death proportion is relatively high. For breast cancer patients, the recurrence ratio is rare, yet it still happens. The highest risk of recurrence usually happens at the early time of treatment, and its severeness depends on the diagnosed stage and the size of the tumor. However, the recurrence prognostic of breast cancer still limited due to the poor prediction strength of input data. Therefore, in this study we used four independent data sets (n = 793) with the same chipsets (HG-U133A) of breast cancer patients to investigate a 67 gene signature used in further building the prognostic model for breast cancer patients. Our results finally confirmed that a 67 gene set was significantly associated with the recurrence (RFS) (p = 1.66e−17), and overall survival (OS) (p = 9.27e−06). Besides, the gene signature also correlated with survival outcomes, such as RFS (p = 0.00167), and distant metastasis-free survival (DMFS) (p < 0.001). Remarkably, our classification analysis results reveal that the breast cancer patients who were categorized into the low-risk group tend to have significantly associated with the longer recurrence time in both training and validation data sets. In conclusion, the 67 gene signature should be considered as potential candidates to be used in building up models or integrated into the current treatment regime for breast cancer.

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