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Open AccessArticle

Circular RNAs in Clear Cell Renal Cell Carcinoma: Their Microarray-Based Identification, Analytical Validation, and Potential Use in a Clinico-Genomic Model to Improve Prognostic Accuracy

1
Department of Urology, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
2
Berlin Institute for Urologic Research, 10115 Berlin, Germany
3
Institute of Pathology, University of Rostock, 18055 Rostock, Germany
4
Institute of Pathology, Hospital Leverkusen, 51375 Leverkusen, Germany
5
Max Delbrueck Center for Molecular Medicine in the Helmholtz Association, Cancer Research Program, 13125 Berlin, Germany
*
Author to whom correspondence should be addressed.
These authors share senior authorship.
Cancers 2019, 11(10), 1473; https://doi.org/10.3390/cancers11101473
Received: 9 August 2019 / Revised: 17 September 2019 / Accepted: 23 September 2019 / Published: 30 September 2019
(This article belongs to the Special Issue Renal Cell Carcinoma)
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. View Full-Text
Keywords: clear cell renal cell carcinoma; identification of circular RNAs; experimental validation of circular RNA; diagnostic and prognostic markers; circular RNAs in a clinico-genomic predictive model; cancer-specific survival; recurrence-free survival; overall survival clear cell renal cell carcinoma; identification of circular RNAs; experimental validation of circular RNA; diagnostic and prognostic markers; circular RNAs in a clinico-genomic predictive model; cancer-specific survival; recurrence-free survival; overall survival
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Franz, A.; Ralla, B.; Weickmann, S.; Jung, M.; Rochow, H.; Stephan, C.; Erbersdobler, A.; Kilic, E.; Fendler, A.; Jung, K. Circular RNAs in Clear Cell Renal Cell Carcinoma: Their Microarray-Based Identification, Analytical Validation, and Potential Use in a Clinico-Genomic Model to Improve Prognostic Accuracy. Cancers 2019, 11, 1473.

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