Bioinformatics analysis of potential prognostic biomarkers among Krüppel-like transcription Factors (KLFs) in breast cancer.

BACKGROUND: Breast cancer is a worldwide leading cause of cancer mortality and it is associated with numerous tumor suppressor genes and oncogenes. Growing evidence exists that different KLFs play pivotal roles of in human malignancies. However, the function of KLFs in breast cancer development has remained uncovered. OBJECTIVE: To explore the potential prognostic biomarkers among KLFs in breast cancer. METHODS: In the present study, by using multiple large open databases, such as Oncomine database, Kaplan-Meier Plotter, and bc-GenExMiner online software, we deeply analyzed the expressions and clinical values about KLFs in patients with breast cancer. RESULTS: KLF4/5/8/9/10/15 were significantly down-regulated in breast cancer samples. KLF11 exerts significantly negative effect on the prognosis of patients, whereas expressions of KLF4/15 were associated with better prognosis. Moreover, the vital genes KLF4/11/15 showed significant association with clinical parameters including age, estrogen receptor, progesterone receptor, epidermal growth factor receptor-2, Scarff-Bloom-Richardson grade, and Nottingham prognostic index. CONCLUSIONS: Bioinformatics analysis suggested that KLF4/11/15, compared to other KLFs, might be potential prognostic indicators and treatment targets for breast cancer patients.