MiR-16 family as potential diagnostic biomarkers for cancer: a systematic review and meta-analysis

Abstract

Background: Cancer is a main public health problem all over the world with its high morbidity and mortality. MicroRNA-16 (miRNA-16, miR-16) family members have been considered as potential biomarkers in cancer diagnosis in several previous studies, but their results were inconsistent.

Objective: The present meta-analysis was conducted to assess the diagnostic efficacy of miR-16 family for cancer systematically.

Methods: Multiple search strategies and random-effects model were used. Pooled sensitivity, specificity and other parameters were calculated. Totally, 1,259 cancer patients and 855 controls from 16 articles were enrolled in this meta-analysis.

Results: The pooled results for sensitivity, specificity, positive likelihood ratios (PLR), negative likelihood ratios (NLR), diagnostic odds ratio (DOR) and the area under the curve (AUC) were 0.80 (95% CI: 0.73-0.85), 0.77 (95% CI: 0.70-0.84), 3.5 (95% CI: 2.5-5.0), 0.26 (95% CI: 0.19-0.36), 14 (95% CI: 8-25) and 0.86 (95% CI: 0.82-0.88), respectively. Our subgroup analyses indicated miR-16 family assay was more appropriate in Asian populations.

Conclusions: Our findings demonstrated that miR-16 family members have a relatively high value as promising biomarkers in diagnosing cancers. Nevertheless, the clinical application of miR-16 family profiling for cancers diagnosis still needs further large-scale studies and additional improvements of substantiation.

Keywords: MicroRNA-16 family; cancer; diagnosis; meta-analysis.

Figure 1

Flow diagram of eligible studies selection process.

Figure 2

Forest plot of mean sensitivity (A) and specificity (B) for miR-16 family assay in cancer detection of each study included.

Figure 3

The SROC of the miR-16 family assay for cancer diagnosis (A: SROC for Asian population; B: SROC for Caucasian population; C: SROC for healthy controls; D: SROC for controls with other disease).

Figure 4

Univariate meta-regression and subgroup analyses for sensitivity and specificity.

Figure 5

Graphical depiction of residual-based goodness-of-fit (A), bivariate normality (B), influence (C) and outlier detection (D).

Figure 6

Deeks’ funnel plot with superimposed regression line in this meta-analysis.

Figure 7

Fagan’s nomogram for estimating post-test possibilities.