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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

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Item Type:Article
Title: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
Creators Name:Franz, A. and Ralla, B. and Weickmann, S. and Jung, M. and Rochow, H. and Stephan, C. and Erbersdobler, A. and Kilic, E. and Fendler, A. and Jung, K.
Abstract: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.
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
Source:Cancers
ISSN:2072-6694
Publisher:Multidisciplinary Digital Publishing Institute (Switzerland)
Volume:11
Number:10
Page Range:1473
Date:30 September 2019
Official Publication:https://doi.org/10.3390/cancers11101473
PubMed:View item in PubMed

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