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Meta-Analysis
. 2017 Apr 25;8(17):28660-28671.
doi: 10.18632/oncotarget.15638.

Circulating miR-31 as an effective biomarker for detection and prognosis of human cancer: a meta-analysis

Yingjun Ma  1 , Yunfang Chen  2 , Jinbo Lin  3 , Yi Liu  4 , Kai Luo  5 , Yong Cao  4 , Tieqiang Wang  4 , Hongwei Jin  4 , Zhan Su  4 , Haolin Wu  4 , Xiaoliang Chen  4 , Jinquan Cheng  6
Affiliations
Meta-Analysis

Circulating miR-31 as an effective biomarker for detection and prognosis of human cancer: a meta-analysis

Yingjun Ma et al. Oncotarget. .

Abstract

Purpose: Circulating miR-31 was found to be associated with cancers detection and prognosis. The present meta-analysis aimed to explore the effect of circulating miR-31 on cancer detection and prognosis.

Method: The studies were accessed using multiple databases. RevMan5.3, Meta-DiSc 1.4, and STATA14.0 were used to estimate the pooled effects, heterogeneity among studies, and publication bias.

Results: A total of 14 studies with 1397 cancer patients and 1039 controls were included. For the 12 prognostic tests, the adjusted pooled-AUC was 0.79 (95% CI: 0.73-0.86) as the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odd ratio (DOR) from 10 tests was 0.79 (95% CI: 0.76-0.82), 0.79 (95% CI: 0.76-0.82), 3.81 (95% CI: 2.90-5.01), 0.26 (95% CI: 0.20-0.35), and 16.81 (95% CI: 9.67-29.25), respectively. For the 5 prognosis analyses, the pooled HR (hazard ratio) of overall survival (OS) was 1.55 (95% CI 1.30-1.86) for high versus low circulating miR-31 expression. However, high expression of circulating miR-31 did not significantly increase the risk of poor differentiation (pooled OR=1.39, 95% CI: 0.56-3.47) and LNM (pooled OR=3.46, 95% CI: 0.96-12.42) in lung cancer.

Conclusion: Circulating miR-31 is an effective biomarker and could be used as a component of miRs signature for cancer detection and prognosis surveillance.

Keywords: carcinoma; detection; meta-analysis; miR-31; prognosis.

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Conflict of interest statement

CONFLICTS OF INTEREST

Xiaoliang Chen, Yi Liu and Yong Cao have received research grants from Shenzhen Technology Research and Development Funds (JCYJ20150403095530583, 201604130086). Yunfang Chen has supported by Shenzhen Technology Research and Development Funds (JCYJ20140416094256880 and JCYJ20160428180919224). Jinbo Lin has supported by Shenzhen Technology Research and Development Funds (JCYJ20140411150916744) and the Science & Technology Project of Shenzhen Longgang District (201406063001026). Other authors declared no interests.

Figures

Figure 1
Figure 1. The flow chart of meta-analysis
Figure 2
Figure 2. pooled AUC of circulating miR-31 test for the diagnosis of various cancers
Abbreviations: AUC, area under receiver operating characteristic curve; SE, standard error; IV, inverse variance methods; CI, confidence interval.
Figure 3
Figure 3. The SROC curve of circulating miR-31 test for the diagnosis of various cancers
Figure 4
Figure 4. Fagan diagram evaluating the overall diagnostic value of miR-31 for cancer
Figure 5
Figure 5. Forest plot of association between circulating miR-31 expression and OS
Abbreviations: SE, standard error; IV, inverse variance methods; CI, confidence interval.
Figure 6
Figure 6. Deek's funnel plot to evaluate the publication bias of test accuracy
See this image and copyright information in PMC

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