Evidence map›Paper›PMID 26812572›Full record

ArticleBritish journal of cancer2016

A urinary microRNA signature can predict the presence of bladder urothelial carcinoma in patients undergoing surveillance.

Nikhil Sapre, Geoff Macintyre, Michael Clarkson, Haroon Naeem, Marek Cmero, Adam Kowalczyk, Paul D Anderson, Anthony J Costello, Niall M Corcoran, Christopher M Hovens

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PubMed Full text at PMC Publisher
In one paragraph

Article in British journal of cancer, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

48 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  13. The Urobiome and Its Role in Overactive Bladder.International neurourology journal · 2022
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  14. The Role of Novel Bladder Cancer Diagnostic and Surveillance Biomarkers-What Should a Urologist Really Know?International journal of environmental research and public health · 2022
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Nikhil SapreDepartment of Surgery, Division of Urology, Royal Melbourne Hospital, The University of Melbourne, Parkville, Melbourne, Victoria, Australia.
Geoff MacintyreNICTA Victoria Research Laboratory, Department of Electronic Engineering, University of Melbourne, Melbourne, Victoria, Australia.
Michael ClarksonDepartment of Surgery, Division of Urology, Royal Melbourne Hospital, The University of Melbourne, Parkville, Melbourne, Victoria, Australia.
Haroon NaeemNICTA Victoria Research Laboratory, Department of Electronic Engineering, University of Melbourne, Melbourne, Victoria, Australia.
Marek CmeroNICTA Victoria Research Laboratory, Department of Electronic Engineering, University of Melbourne, Melbourne, Victoria, Australia.
Adam KowalczykNICTA Victoria Research Laboratory, Department of Electronic Engineering, University of Melbourne, Melbourne, Victoria, Australia.
Paul D AndersonDepartment of Surgery, Division of Urology, Royal Melbourne Hospital, The University of Melbourne, Parkville, Melbourne, Victoria, Australia.
Anthony J CostelloDepartment of Surgery, Division of Urology, Royal Melbourne Hospital, The University of Melbourne, Parkville, Melbourne, Victoria, Australia.
Niall M CorcoranDepartment of Surgery, Division of Urology, Royal Melbourne Hospital, The University of Melbourne, Parkville, Melbourne, Victoria, Australia.
Christopher M HovensDepartment of Surgery, Division of Urology, Royal Melbourne Hospital, The University of Melbourne, Parkville, Melbourne, Victoria, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe objective of this study was to determine whether microRNA (miRNA) profiling of urine could identify the presence of urothelial carcinoma of the bladder (UCB) and to compare its performance characteristics to that of cystoscopy.

methodsIn the discovery cohort we screened 81 patients, which included 21 benign controls, 30 non-recurrers and 30 patients with active cancer (recurrers), using a panel of 12 miRNAs. Data analysis was performed using a machine learning approach of a Support Vector Machine classifier with a Student's t-test feature selection procedure. This was trained using a three-fold cross validation approach and performance was measured using the area under the receiver operator characteristic curve (AUC). The miRNA signature was validated in an independent cohort of a further 50 patients.

resultsThe best predictor to distinguish patients with UCB from non-recurrers was achieved using a combination of six miRNAs (AUC=0.85). This validated in an independent cohort (AUC=0.74) and detected UCB with a high sensitivity (88%) and sufficient specificity (48%) with all significant cancers identified. The performance of the classifier was best in detecting clinically significant disease such as presence of T1 Stage disease (AUC=0.92) and high-volume disease (AUC=0.81). Cystoscopy rates in the validation cohort would have been reduced by 30%.

conclusionsUrinary profiling using this panel of miRNAs shows promise for detection of tumour recurrence in the surveillance of UCB. Such a panel may be useful in reducing the morbidity and costs associated with cystoscopic surveillance, and now merits prospective evaluation.

Indexed as

Biomarkers, TumorCase-Control StudiesCohort StudiesCystoscopyHumansMicroRNAsPrognosisUrinary Bladder NeoplasmsBiomarkers, TumorMicroRNAs

Identifiers

PMID26812572
PMCPMC4815774

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LicenceCC BY-NC-SA
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Registered trials

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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.