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Authors Tan H, Gan L, Fan X, Liu L, Liu S
Received 8 December 2018
Accepted for publication 27 February 2019
Published 10 April 2019 Volume 2019:12 Pages 2623—2633
DOI https://doi.org/10.2147/OTT.S197537
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Amy Norman
Peer reviewer comments 2
Editor who approved publication: Dr Sanjeev Srivastava
Background: Increasing
evidence has identified circular RNAs (circRNAs) as ideal molecular biomarkers
for cancer diagnosis, therapy, and prognosis. However, the overall diagnostic
efficiency of circRNAs remains unclear. Thus, this meta-analysis aimed to
comprehensively evaluate the diagnostic accuracy of circRNA expression profiles
for cancer.
Methods: A literature
search of online databases was conducted to identify all eligible studies. The
quality of the studies was assessed using the Quality Assessment of Diagnostic
Accuracy Studies 2 tool. All statistical analyses were executed using STATA
14.0, Meta-DiSc 1.4, and Review Manager 5.2 software.
Results: A total
of 32 studies, involving 2,400 cases and 2,295 controls, were included in the
diagnostic meta-analysis. The pooled sensitivity, specificity, positive
likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area
under the curve were 0.79 (95% CI: 0.73–0.84), 0.73 (95% CI: 0.67–0.79), 2.9
(95% CI: 2.5–3.5), 0.29 (95% CI: 0.24–0.36), 10 (95% CI: 8–13), and 0.83 (95%
CI: 0.79–0.86), respectively. The overall analysis suggested that circRNAs are
useful diagnostic biomarkers for cancer. Subgroup analysis indicated that
plasma samples had a better diagnostic performance than cancer tissue samples
for cancer detection. Studies involving ≥100 cases or gastric cancer showed
higher sensitivities than those including <100 cases or other cancers.
Conclusion: This
meta-analysis revealed that circRNAs were significantly correlated with cancer
diagnosis. In addition, circRNAs had good diagnostic accuracy and might serve
as effective diagnostic biomarkers for cancer.
Keywords: circular
RNAs, cancer, diagnosis, biomarkers, meta-analysis
