%0 Journal Article
%T Identification of circulating microRNAs as diagnostic biomarkers for ovarian cancer: A pooled analysis of individual studies
%A Hai-Rong Li
%A Man-Zhen Zuo
%A Quan Zhou
%A Wei Li
%A Ze He
%J The International Journal of Biological Markers
%@ 1724-6008
%D 2018
%R 10.1177/1724600818766500
%X Circulating microRNAs (miRNAs) are proposed as promising non-invasive diagnostic biomarkers for many cancers. However, the diagnostic value of circulating miRNAs in ovarian cancer is inconsistent in different studies. Thus we performed this meta-analysis to systematically evaluate the diagnostic value of circulating miRNAs in ovarian cancer. Eligible studies that were published prior to 30 June 2017 were searched from the PubMed, EMBASE, Cochrane Library, and Chinese National Knowledge Infrastructure. All analyses were performed using STATA 12.0 software. A bivariate regression was used to calculate pooled diagnostic accuracy estimates. A total of 36 studies from 16 publications were included in this meta-analysis. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of circulating miRNAs for ovarian cancer diagnosis were 0.76 (95% confidence intervals (CI): 0.69, 0.81), 0.81 (95% CI 0.74, 0.87), 4.00 (95% CI 2.70, 5.30), 0.30(95% CI 0.24, 0.37) and 13.00 (95% CI 9.00, 19.00), respectively. The area under the summary receiver operating characteristic curve was 0.85 (95% CI 0.82, 0.88). Subgroup analyses showed that multiple miRNA assays yielded better diagnostic characteristics than a single miRNA assay, and plasma miRNAs were better than serum miRNAs for ovarian cancer detection. Circulating miRNAs, especially the combination of multiple circulating miRNAs, are promising biomarkers for the diagnosis of ovarian cancer. However, further large-scale prospective studies are necessary to validate the applicability of the miRNAs in the early detection of ovarian cancer
%K MicroRNAs
%K Ovarian Cancer
%K Diagnosis
%K Meta-analysis
%U https://journals.sagepub.com/doi/full/10.1177/1724600818766500