4.7 Article

Decreased expression of microRNA-148a predicts poor prognosis in ovarian cancer and associates with tumor growth and metastasis

Journal

BIOMEDICINE & PHARMACOTHERAPY
Volume 83, Issue -, Pages 58-63

Publisher

ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER
DOI: 10.1016/j.biopha.2016.05.049

Keywords

MiR-148a; Ovarian cancer; Prognosis; Metastasis

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Objective: MicroRNA-148a (MiR-148a) had been reported to take part in some cancer progresses, but its clinical significance in ovarian cancer had been rarely reported. The purpose of this study was to evaluate the prognostic value of miR-148a as well as its roles in ovarian cancer progression. Methods: Relative expression of miR-148a in the plasma specimens of ovarian cancer patients was detected by qRT-PCR. Chi-square test was used to analyze the relationship between miR-148a expression and clinical characteristics. The overall survival was analyzed by Kaplan-Meier method and Cox regression analysis was used to evaluate the prognostic value of miR-148a. In addition, the ovarian cancer cell line SKOV-3 was separately transfected with pcDNA3-microRNA-148a over-expression vector and pcDNA3 empty vector to detect the functional roles of miR-148a in ovarian cancer progression. Results: Decreased level of plasma miR-148a was observed in ovarian cancer patients compared with healthy controls. The expression level was associated with histopathologic grade, TNM stage and lymph node metastasis (P < 0.05 for all). Besides, patients with high level of miR-148a had a longer survival time than those with low level (40.3 months vs 31.6 months, log rank test, P = 0.002). Cox regression analysis indicated that miR-148a might be a potential biomarker for ovarian cancer prognosis (HR = 1.699, 95% CI = 1.175-2.456, P = 0.005). Moreover, cell experiments confirmed that miR-148a could inhibit proliferation, migration and invasion of ovarian cancer cells. Conclusion: MiR-148a may be a potential prognostic factor for ovarian cancer and it can suppress tumor progression. (C) 2016 Published by Elsevier Masson SAS.

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