dc.contributor.author
Franz, Antonia
dc.contributor.author
Ralla, Bernhard
dc.contributor.author
Weickmann, Sabine
dc.contributor.author
Jung, Monika
dc.contributor.author
Rochow, Hannah
dc.contributor.author
Stephan, Carsten
dc.contributor.author
Erbersdobler, Andreas
dc.contributor.author
Kilic, Ergin
dc.contributor.author
Fendler, Annika
dc.contributor.author
Jung, Klaus
dc.date.accessioned
2020-01-20T13:57:07Z
dc.date.available
2020-01-20T13:57:07Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/26458
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-26218
dc.description.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.
en
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
clear cell renal cell carcinoma
en
dc.subject
identification of circular RNAs
en
dc.subject
experimental validation of circular RNA
en
dc.subject
diagnostic and prognostic markers
en
dc.subject
circular RNAs in a clinico-genomic predictive model
en
dc.subject
cancer-specific survival
en
dc.subject
recurrence-free survival
en
dc.subject
overall survival
en
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.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
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
1473
dcterms.bibliographicCitation.doi
10.3390/cancers11101473
dcterms.bibliographicCitation.journaltitle
Cancers
dcterms.bibliographicCitation.number
10
dcterms.bibliographicCitation.originalpublishername
MDPI AG
dcterms.bibliographicCitation.volume
11
refubium.affiliation
Charité - Universitätsmedizin Berlin
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.bibliographicCitation.pmid
31575051
dcterms.isPartOf.eissn
2072-6694