Circular RNAs (circRNAs) may act as novel cancer biomarkers. However, a genome-wide evaluation of circRNAs in clear cell renal cell carcinoma (ccRCC) has yet to be conducted. Therefore, the objective of this study was to identify and validate circRNAs in ccRCC tissue with a focus to evaluate their potential as prognostic biomarkers. A genome-wide identification of circRNAs in total RNA extracted from ccRCC tissue samples was performed using microarray analysis. Three relevant differentially expressed circRNAs were selected (circEGLN3, circNOX4, and circRHOBTB3), their circular nature was experimentally confirmed, and their expression—along with that of their linear counterparts—was measured in 99 malignant and 85 adjacent normal tissue samples using specifically established RT-qPCR assays. The capacity of circRNAs to discriminate between malignant and adjacent normal tissue samples and their prognostic potential (with the endpoints cancer-specific, recurrence-free, and overall survival) after surgery were estimated by C-statistics, Kaplan-Meier method, univariate and multivariate Cox regression analysis, decision curve analysis, and Akaike and Bayesian information criteria. CircEGLN3 discriminated malignant from normal tissue with 97% accuracy. We generated a prognostic for the three endpoints by multivariate Cox regression analysis that included circEGLN3, circRHOBT3 and linRHOBTB3. The predictive outcome accuracy of the clinical models based on clinicopathological factors was improved in combination with this circRNA-based signature. Bootstrapping as well as Akaike and Bayesian information criteria confirmed the statistical significance and robustness of the combined models. Limitations of this study include its retrospective nature and the lack of external validation. The study demonstrated the promising potential of circRNAs as diagnostic and particularly prognostic biomarkers in ccRCC patients.

As new biomarkers, circular RNAs (circRNAs) have been largely unexplored in prostate cancer (PCa). Using an integrative approach, we aimed to evaluate the potential of circRNAs and their linear transcripts (linRNAs) to act as (i) diagnostic biomarkers for differentiation between normal and tumor tissue and (ii) prognostic biomarkers for the prediction of biochemical recurrence (BCR) after radical prostatectomy. In a first step, eight circRNAs (circATXN10, circCRIM1, circCSNK1G3, circGUCY1A2, circLPP, circNEAT1, circRHOBTB3, and circSTIL) were identified as differentially expressed via a genome-wide circRNA-based microarray analysis of six PCa samples. Additional bioinformatics and literature data were applied for this selection process. In total, 115 malignant PCa and 79 adjacent normal tissue samples were examined using robust RT-qPCR assays specifically established for the circRNAs and their linear counterparts. Their diagnostic and prognostic potential was evaluated using receiver operating characteristic curves, Cox regressions, decision curve analyses, and C-statistic calculations of prognostic indices. The combination of circATXN10 and linSTIL showed a high discriminative ability between malignant and adjacent normal tissue PCa. The combination of linGUCY1A2, linNEAT1, and linSTIL proved to be the best predictive RNA-signature for BCR. The combination of this RNA signature with five established reference models based on only clinicopathological factors resulted in an improved predictive accuracy for BCR in these models. This is an encouraging study for PCa to evaluate circRNAs and their linRNAs in an integrative approach, and the results showed their clinical potential in combination with standard clinicopathological variables.