Purpose: Although microRNAs have recently been recognized as riboregulators of gene expression, little is known about microRNA expression profiles in serous ovarian carcinoma. We assessed the expression of microRNA and the association between microRNA expression and the prognosis of serous ovarian carcinoma. Experimental Design: Twenty patients diagnosed with serous ovarian carcinoma and eight patients treated for benign uterine disease between December 2000 and September 2003 were enrolled in this study. The microRNA expression profiles were examined using DNA microarray and Northern blot analyses. Results: Several microRNAs were differentially expressed in serous ovarian carcinoma compared with normal ovarian tissues, including miR-21, miR-125a, miR-125b, miR-100, miR-145, miR-16, and miR-99a, which were each differentially expressed in >16 patients. In addition, the expression levels of some microRNAs were correlated with the survival in patients with serous ovarian carcinoma. Higher expression of miR-200, miR-141, miR-18a, miR-93, and miR-429, and lower expression of let-7b, and miR-199a were significantly correlated with a poor prognosis (P < 0.05). Conclusion:Our results indicate that dysregulation of microRNAs is involved in ovarian carcinogenesis and associated with the prognosis of serous ovarian carcinoma.

Our findings provide evidence for the association between NLR and epithelial ovarian cancer. Preoperative NLR, in combination with CA125, may represent a simple and cost-effective method of identifying ovarian cancers, and an elevated NLR may predict an adverse outcome in ovarian cancer.

Recent studies posit a role for non-coding RNAs in epithelial ovarian cancer (EOC). Combining small RNA sequencing from 179 human serum samples with a neural network analysis produced a miRNA algorithm for diagnosis of EOC (AUC 0.90; 95% CI: 0.81-0.99). The model significantly outperformed CA125 and functioned well regardless of patient age, histology, or stage. Among 454 patients with various diagnoses, the miRNA neural network had 100% specificity for ovarian cancer. After using 325 samples to adapt the neural network to qPCR measurements, the model was validated using 51 independent clinical samples, with a positive predictive value of 91.3% (95% CI: 73.3-97.6%) and negative predictive value of 78.6% (95% CI: 64.2-88.2%). Finally, biologic relevance was tested using in situ hybridization on 30 pre-metastatic lesions, showing intratumoral concentration of relevant miRNAs. These data suggest circulating miRNAs have potential to develop a non-invasive diagnostic test for ovarian cancer.

Pazopanib maintenance therapy provided a median improvement of 5.6 months (HR, 0.77) in progression-free survival in patients with advanced ovarian cancer who have not progressed after first-line chemotherapy. Overall survival data to this point did not suggest any benefit. Additional analysis should help to identify subgroups of patients in whom improved efficacy may balance toxicity (NCT00866697).