Haiyan Li, Yuping Xu, Weifeng Qiu, Danni Zhao, Yuanzhen Zhang
Department of Gynecology and Obstetrics, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China (mainland)
Med Sci Monit 2015; 21:3929-3934
MiRNAs play important roles in regulating many fundamental biological processes. Deregulation of miRNAs is involved in the initiation and progression of cancer. MiR-193b is regarded as tumor suppressor in many types of cancers. However, the role of miR-193b in ovarian cancer is poorly understood.
MATERIAL AND METHODS: The expression level of miR-193b in ovarian cancer cell lines and ovarian cancer samples was evaluated using quantitative real-time reverse transcription-PCR (qRT-PCR). The ovarian cancer patients were categorized into a high miR-193b expression group and a low miR-193b expression group according to the median miR-193b expression level. The correlation between tissue miR-193b expression and the patients’ clinicopathological factors, as well as survival, was also analyzed.
RESULTS: The results showed that the miR-193b expression was significantly down-regulated in ovarian cancer cell lines and tumor tissues compared with normal controls. In addition, tissue miR-193b expression was positively correlated with FIGO stage (P=0.001), histological grade (P=0.032), ascites (P=0.019), lymph node metastasis (P=0.003), and tumor size (P=0.041). Among 116 patients with ovarian cancer examined, the 5-year overall survival (OS) rates were 62.5% and 22.01% in patients with high and low miR-193b expression, respectively (P=0.003). Multivariate analysis showed that tissue miR-193b is an independent prognostic factor in patients with ovarian cancer (HR=4.219; P=0.015).
CONCLUSIONS: Reduction of miR-193b was found in ovarian cancer and its lower expression was associated with poorer prognosis. Tissue miR-193b showed potential as novel biomarker for ovarian cancer.
Keywords: Cell Line, Tumor, Biomarkers, Tumor - metabolism, Down-Regulation, Early Diagnosis, MicroRNAs - metabolism, Ovarian Neoplasms - pathology, Risk Factors, Survival Analysis