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Meta-Analysis
. 2021 Dec 1;19(1):177.
doi: 10.1186/s12958-021-00852-8.

Circulating noncoding RNAs as early predictive biomarkers in preeclampsia: a diagnostic meta-analysis

Affiliations
Meta-Analysis

Circulating noncoding RNAs as early predictive biomarkers in preeclampsia: a diagnostic meta-analysis

Sha Su et al. Reprod Biol Endocrinol. .

Abstract

Background: We designed a meta-analysis to evaluate the clinical significance and efficacy of circulating noncoding RNAs (ncRNAs) in the early prediction of preeclampsia.

Methods: PubMed, Embase and the Cochrane Library were used to search for literature. The combined prediction performance was evaluated by calculating the area under the summary receiver operator characteristic (SROC) curve. The potential sources of heterogeneity were analysed by meta-regression analysis and subgroup analysis. All statistical analyses and mapping were performed by RevMan 5.3 and Stata 12.0.

Results: A total of 41 studies from 14 articles, including 557 preeclampsia patients and 842 controls, were included in our meta-analysis. All studies collected blood before onset. NcRNAs in blood performed relatively well in predicting preeclampsia. The combined sensitivity was 0.71, the specificity was 0.84, and the area under the SROC curve (AUC) was 0.86. Peripheral blood mononuclear cell (PBMC) samples showed the best diagnostic accuracy. The combined AUC was 0.93. Combined detection was better than single detection, and miRNA was better than circRNA. The heterogeneity of the study was determined by sample size, lncRNA characteristics, lncRNA source and race.

Conclusion: Circulating ncRNAs can be valuable biomarkers used as candidates for noninvasive early predictive biomarkers of preeclampsia and have great clinical application prospects. The clinical value of ncRNAs needs to be tested by further multicentre, comprehensive and prospective studies, and the test criteria should be established.

Keywords: Biomarkers; Diagnostic meta-analysis; Preeclampsia; ncRNAs.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Flow chart for selection of eligible articles
Fig. 2
Fig. 2
Forest plots of the pooled sensitivity and specificity of overall studies
Fig. 3
Fig. 3
Predictive performance of circulating lncRNAs of overall studies for PE. (a) SROC curve. (b) Fagan’s nomogram
Fig. 4
Fig. 4
SROC curves based on predictive studies of (a) miRNA, (b) combined ncRNA assay, and (c) PBMCs
Fig. 5
Fig. 5
Regression analysis and subgroup analysis
Fig. 6
Fig. 6
Impact analysis and outlier detection. (a) Goodness of fit (b) bivariate normality (c) impact analysis, and (d) outlier detection
Fig. 7
Fig. 7
Deeks’ funnel plot symmetry test for publication bias

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