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World Journal of Surgical Oncology

, 15:147

First Online: 03 August 2017Received: 16 August 2016Accepted: 08 July 2017


BackgroundPrevious studies have revealed the importance of microRNAs’ miRNAs function as biomarkers in diagnosing human bladder cancer BC. However, the results are discordant. Consequently, the possibility of miRNAs to be BC biomarkers was summarized in this meta-analysis.

MethodsIn this study, the relevant articles were systematically searched from CBM, PubMed, EMBASE, and Chinese National Knowledge Infrastructure CNKI. The bivariate model was used to calculate the pooled diagnostic parameters and summary receiver operator characteristic SROC curve in this meta-analysis, thereby estimating the whole predictive performance. STATA software was used during the whole analysis.

ResultsThirty-one studies from 10 articles, including 1556 cases and 1347 controls, were explored in this meta-analysis. In short, the pooled sensitivity, area under the SROC curve, specificity, positive likelihood ratio, diagnostic odds ratio, and negative likelihood ratio were 0.72 95%CI 0.66–0.76, 0.80 0.77–0.84, 0.76 0.71–0.81, 3.0 2.4–3.8, 8 5.0–12.0, and 0.37 0.30–0.46 respectively. Additionally, sub-group and meta-regression analyses revealed that there were significant differences between ethnicity, miRNA profiling, and specimen sub-groups. These results suggested that Asian population-based studies, multiple-miRNA profiling, and blood-based assays might yield a higher diagnostic accuracy than their counterparts.

ConclusionsThis meta-analysis demonstrated that miRNAs, particularly multiple miRNAs in the blood, might be novel, useful biomarkers with relatively high sensitivity and specificity and can be used for the diagnosis of BC. However, further prospective studies with more samples should be performed for further validation.

KeywordsDiagnosis miRNA Bladder cancer Meta-analysis AbbreviationsBCBladder cancer

BTABladder tumor antigen

CIsConfidence intervals

DORDiagnostic odds ratio

FNFalse negatives

FPFalse positives

MIBCMuscle-invasive BC


NLRNegative likelihood ratio

NMIBCNon-muscle-invasive BC

NMP22Nuclear matrix protein 22

PLRPositive likelihood ratio

SROCSummary receiver operator characteristic

TNTrue negatives

TPTrue positives

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Autor: Hong-Bin Shi - Jia-Xing Yu - Jian-Xiu Yu - Zheng Feng - Chao Zhang - Guang-Yong Li - Rui-Ning Zhao - Xiao-Bo Yang

Fuente: https://link.springer.com/

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