Identification of microRNAs as novel biomarkers for glioma detection: a meta-analysis based on 11 articles

Glioma is the most common and lethal primary brain tumors, and is regarded as one of the deadliest of human cancers. To date, a growing number of studies have tested the diagnostic accuracy of microRNAs (miRNAs) in glioma detection and altered levels of characteristic miRNAs have also been identified in glioma. However, there are some conflicting conclusions. Thus, we conducted this meta-analysis to evaluate the overall accuracy of miRNAs in the diagnosis of glioma.

A comprehensive literature search was conducted using a combination of keywords. The random effect model was used to calculate the pooled diagnostic parameters. The summary receiver operator characteristic (SROC) curves were plotted to assess the overall diagnostic performance of miRNAs. Subgroup and sensitivity analyses were conducted to analyze the potential sources of heterogeneity.

In total, 28 studies from 11 articles covering 1729 patients and 1491 controls were available in this meta-analysis. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.87 (95% CI: 0.83-0.91), 0.87 (95% CI: 0.81-0.91), 6.6 (95% CI: 4.5-9.6), 0.15 (95% CI: 0.10-0.21), 45 (95% CI: 23-90), and 0.93 (95% CI: 0.91-0.95), respectively. Subgroup analysis demonstrated that panels of multiple miRNAs could largely improve the diagnostic accuracy. An independent meta-analysis of five included studies was conducted to evaluate the diagnostic efficacy of miR-21 in patients with glioma, with a pooled sensitivity of 0.82, specificity of 0.94, PLR of 13.2, NLR of 0.19, DOR of 69 and AUC of 0.95.

This meta-analysis indicated the great potential of miRNAs, especially panels of multiple miRNAs, as promising biomarkers in glioma detection and monitoring. As one of the most representative miRNAs, we also found that a single miR-21 could be a powerful clinical biomarker in glioma diagnosis.