Evidence map›Paper›PMID 31575051›Full record

ArticleCancers2019

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.

Antonia Franz, Bernhard Ralla, Sabine Weickmann, Monika Jung, Hannah Rochow, Carsten Stephan, Andreas Erbersdobler, Ergin Kilic, Annika Fendler, Klaus Jung

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
3.5field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.

  1. Pooled it
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  12. Circular RNAs in kidney disease and cancer.Nature reviews. Nephrology · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 4 institutions in 1 country.

Antonia FranzDepartment of Urology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany. antonia.franz@charite.de.
Bernhard RallaDepartment of Urology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany. bernhard.ralla@charite.de.
Sabine WeickmannDepartment of Urology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany. sabine.weickmann@charite.de.
Monika JungDepartment of Urology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany. mchjung94@gmail.com.
Hannah RochowDepartment of Urology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany. hannah.rochow@charite.de.
Carsten StephanDepartment of Urology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany. carsten.stephan@charite.de.
Andreas ErbersdoblerInstitute of Pathology, University of Rostock, 18055 Rostock, Germany. andreas.erbersdobler@med.uni-rostock.de.
Ergin KilicInstitute of Pathology, Hospital Leverkusen, 51375 Leverkusen, Germany. e.kilic@pathologie-leverkusen.de.
Annika FendlerDepartment of Urology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany. annika.fendler@mdc-berlin.de.
Klaus JungDepartment of Urology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany. klaus.jung@charite.de.
Charité - Universitätsmedizin Berlin · DEEcologic Institute · DEKlinikum Leverkusen · DEUniversity of Rostock · DE

Funding

Foundation Urologic Research, Berlin, Germany BFIU2017/18-AF
6 · The paper itself

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.

Indexed as

cancer-specific survivalcircular RNAs in a clinico-genomic predictive modelclear cell renal cell carcinomadiagnostic and prognostic markersexperimental validation of circular RNAidentification of circular RNAsoverall survivalrecurrence-free survival

Identifiers

PMID31575051
PMCPMC6826865
OpenAlexW2976789237

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.