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. 2019 May 10:11:4313-4326.
doi: 10.2147/CMAR.S199126. eCollection 2019.

Circulating microRNA as diagnostic biomarkers for haematological cancers: a systematic review and meta-analysis

Emmanuel Kwateng Drokow  1 , Kai Sun  1 , Hafiz Abdul Waqas Ahmed  1 , Gloria Selorm Akpabla  2 , Juanjuan Song  1 , Mingyue Shi  1
Affiliations

Circulating microRNA as diagnostic biomarkers for haematological cancers: a systematic review and meta-analysis

Emmanuel Kwateng Drokow et al. Cancer Manag Res. .

Abstract

Purpose: Recent studies have validated microRNAs (miRNAs) as a diagnostic biomarker for haematological cancers. This study aimed to estimate the overall diagnostic accuracy of circulating miRNAs in haematological malignancies. Materials and Methods: Multiple databases (Google Scholar, PubMed, EMBASE, Cochrane Library,) were searched until 19th August 2017. Results: The meta-analysis included 50 studies from 20 publications. The diagnostic accuracy was assessed by pooled specificity, sensitivity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the curve area (AUC) by random effect model. We used QUADAS (Quality Assessment for diagnostic accuracy studies) to evaluate the quality of the included studies. To perform the meta-analysis, we used Meta-Disk 1.4, Revman 5.3 and Stata 12.0 software. High diagnostic accuracy was demonstrated, with a sensitivity of 0.81, a specificity of 0.85, a PLR of 5.28, an NLR of 0.22, a DOR of 30.39, and an AUC of 0.91. Subgroup analyses showed better outcomes for the African population, combined miRNAs and leukaemia patients compared with other subgroups. Conclusion: Our results indicated that circulating miRNAs especially combined miRNA can be used as a diagnostic marker in haematological cancers.

Keywords: Specificity; diagnostic biomarkers; haematological cancers; miRNA; sensitivity.

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Conflict of interest statement

The authors report no conflicts of interest in this work.

Figures

Figure 3
Figure 3
Forest plot of specificity.
Figure 4
Figure 4
Forest plot of the negative likelihood ratio (NLR).
Figure 5
Figure 5
Forest plot of the positive likelihood ratio (PRL).
Figure 6
Figure 6
Forest plot of diagnostic odds ratio (DOR).
Figure 1
Figure 1
Flowchart of studies selection.
Figure 2
Figure 2
Forest plot of sensitivity.
Figure 7
Figure 7
Summary ROC curve for the included studies.
See this image and copyright information in PMC

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