MicroRNA in the diagnosis of lung cancer: An overview of ten systematic reviews

Abstract

Objective To provide theoretical support for clinical diagnosis of lung cancer through an overview of systematic reviews (SRs) of the diagnostic value of miRNA. Methods We searched PubMed, Embase, and the Cochrane Library to collect SRs of the diagnostic value of microRNA for lung cancer until April 2021. A comprehensive database search was carried out, screened, and extracted information independently by two researchers, to compare and analyze the sensitivity and specificity of relevant literatures. The ROBIS tool was applied to assess the risk of bias of included SRs and meta-analysis. Results A total of 10 SRs were included the results of risk of bias assessment by ROBIS tool showed: 10 SRs completely matched the four questions of phase one. In phase two, nine SRs were low risk of bias in the including criteria field; one study was uncertain; nine SRs were low risk of bias, one study was uncertain in the literature search and screening field; nine SRs were low risk of bias in the data abstraction and quality assessment field, one SR was high; and 10 SRs were low risk of bias in the data synthesis field. In the phase three of comprehensive risk of bias results, 8 studies were low risk, one was high risk, and one study was uncertain. MiRNA had a pooled sensitivity of 0.77 (95% CI: 0.73–0.81) and specificity of 0.81 (95% CI: 0.79–0.84). The summarized area under the SROC curve was 0.86 (95% CI: 0.83–0.89), and combined diagnostic odds ratio was 14.68 (10.87–19.81). The sensitivity and specificity of multiple MicroRNA was 0.80 (0.73–0.85) and 0.80 (0.76–0.83); the sensitivity and specificity of single MicroRNA was 0.74 (0.69–0.79) and 0.83 (0.80–0.88). Conclusion MiRNA is a promising biomarker for the diagnosis of lung cancer in Chinese population, with advanced sensitivity and specificity. It provides a faster and less invasive assessment of lung cancer than other markers that require histopathological analysis. We should improve the reliability of the conclusion. The construction of diagnosis for lung cancer provides reliable evidence support.