In silico detection of potential prognostic circRNAs through a re-annotation strategy in ovarian cancer

Ovarian cancer (OC) is the most common and lethal gynecologic malignancy. The pathophysiology of OC tumor development is complex and involves numerous biological pathways. Previous studies suggest that circular (circ)RNAs serve important roles in OC tumor pathology. In the present study, a re-annotation strategy was performed to evaluate the expression level of circRNAs based on a microarray dataset obtained from the Gene Expression Omnibus database. Univariate and multivariate Cox regression analyses were performed to evaluate the association between survival and expression of circRNAs in each OC cohort. An expression-based risk score model was constructed to extrapolate the prognostic efficacy of this signature. In the GSE9891 dataset, the 278 OC patients were randomly divided into training and validating groups. A six-circRNA signature was significantly associated with overall survival in the training and validating datasets. The risk score model was further validated in GSE63885 and GSE26193 datasets. The six-circRNA signature was also significantly associated with patient progression-free survival and disease-free survival. Further investigation revealed that the signature had higher area under the curve values than the existing clinical and other molecular signatures in predicting survival. In conclusion, the present study revealed that the six-circRNA signature may serve as a potential prognostic biomarker of OC.