已发表论文

LncRNA 作为膀胱癌的诊断和预后生物标志物:系统综述和荟萃分析

 

Authors Quan J, Pan X, Zhao L, Li Z, Dai K, Yan F, Liu S, Ma H, Lai Y

Received 10 March 2018

Accepted for publication 20 July 2018

Published 4 October 2018 Volume 2018:11 Pages 6415—6424

DOI https://doi.org/10.2147/OTT.S167853

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Cristina Weinberg

Peer reviewer comments 3

Editor who approved publication: Prof. Dr. Geoffrey Pietersz

Background: Bladder cancer is one of the most common urinary malignancies, and has a high recurrence rate and poor outcomes. In order to identify novel diagnostic and prognostic biomarkers for bladder cancer, we conducted a meta-analysis to analyze the association between long non-coding RNA (lncRNA) expression and survival in bladder cancer.
Materials and methods: We searched literature from databases using our inclusion and exclusion criteria. STATA 14.0 software was used to analyze the data from collected studies and to construct the forest plots. A different effect size was selected for each meta-analysis.
Results: After selection, 30 articles were found to be eligible. The present meta-analysis contains data from 13 articles about clinicopathological characteristics, six articles about diagnosis, and 16 articles about prognosis. In the present study, we found that many lncRNAs could function as potential diagnostic and prognostic markers in bladder cancer. Among these findings, UCA1 was expected to be a diagnostic biomarker for bladder cancer, while the aberrant expression of HOTAIR and GAS5 was associated with poor disease-free survival/recurrence-free survival/disease-specific survival.
Conclusion: Overall, the present study is the first meta-analysis to assess the association between expression of lncRNAs and clinical value in patients with bladder cancer. LncRNAs hold promise as novel diagnostic and prognostic markers in bladder cancer.
Keywords: lncRNA, bladder cancer, clinicopathological characteristics, prognosis




Figure 4 The summary receiver operator characteristic (SROC) curve based on UCA1.