Fig 1.
Flow chart of studies selection and quality assessment of studies.
A) A total of 16 published articles were included in this meta-analysis after filtering through the inclusion criteria. B) QUADAS-2 assessment. Bar chart showing the summary of risk of bias and applicability concerns, expressed as percentage. Each study occupied the bar equally (1/16, 6.25%). Red: high risk; Yellow: unclear risk; Green: low risk.
Table 1.
Characteristics of included circulating miRNA studies in this meta analysis.
Fig 2.
Forest plots of all 16 studies included in this meta-analysis.
The pooled A) sensitivity and B) specificity is 0.87 and 0.86, respectively of miRNAs in diagnosis of glioma.
Fig 3.
Diagram of SROC curves describing the diagnostic performance of miRNAs.
A) The PLR and NLR is 6.39 and 0.15 respectively, showing the pre-test probability set as 25%, the positive and negative post-test probability of 68% and 5%, respectively. B) The AUC is 0.93 (95%CI, 0.91–0.95). Each number within a circle represents the order of study identifier in Fig 3.
Table 2.
Diagnostic characteristics of each single miRNA and miRNA panel.
Fig 4.
The diagram shows the (a) goodness-of-fit, (b) bivariate normality, (c) influence and (d) outlier detection analyses. Goodness-of-fit and bivariate normality showed this analysis fitted the model well. Influence analysis identified the most dominant studies in this meta-analysis. Outlier detection demonstrated one study (D’Urso et al and Xiao et al) is outside the standard residual square. Each number within circle represents the order of study identifier in Fig 3.