Meta-analysis of diagnostic cell-free circulating microRNAs for breast cancer detection

Abstract

Background: Breast cancer (BC) is the most frequently diagnosed cancer among women. Numerous studies explored cell-free circulating microRNAs as diagnostic biomarkers of BC. As inconsistent and rarely intersecting microRNA panels have been reported thus far, we aim to evaluate the overall diagnostic performance as well as the sources of heterogeneity between studies.

Methods: Based on the search of three online search engines performed up to March 21^st 2022, 56 eligible publications that investigated diagnostic circulating microRNAs by utilizing Real-Time Quantitative Reverse Transcription PCR (qRT-PCR) were obtained. Primary studies' potential for bias was evaluated with the revised tool for the quality assessment of diagnostic accuracy studies (QUADAS-2). A bivariate generalized linear mixed-effects model was applied to obtain pooled sensitivity and specificity. A novel methodology was utilized in which the sample and study models' characteristics were analysed to determine the potential preference of studies for sensitivity or specificity.

Results: Pooled sensitivity and specificity of 0.85 [0.81-0.88] and 0.83 [0.79-0.87] were obtained, respectively. Subgroup analysis showed a significantly better performance of multiple (sensitivity: 0.90 [0.86-0.93]; specificity: 0.86 [0.80-0.90]) vs single (sensitivity: 0.82 [0.77-0.86], specificity: 0.83 [0.78-0.87]) microRNA panels and a comparable pooled diagnostic performance between studies using serum (sensitivity: 0.87 [0.81-0.91]; specificity: 0.83 [0.78-0.87]) and plasma (sensitivity: 0.83 [0.77-0.87]; specificity: 0.85 [0.78-0.91]) as specimen type. In addition, based on bivariate and univariate analyses, miRNA(s) based on endogenous normalizers tend to have a higher diagnostic performance than miRNA(s) based on exogenous ones. Moreover, a slight tendency of studies to prefer specificity over sensitivity was observed.

Conclusions: In this study the diagnostic ability of circulating microRNAs to diagnose BC was reaffirmed. Nonetheless, some subgroup analyses showed between-study heterogeneity. Finally, lack of standardization and of result reproducibility remain the biggest issues regarding the diagnostic application of circulating cell-free microRNAs.

Keywords: Breast cancer; Circulating cell-free; Diagnostic; Meta-analysis; miRNAs.

Fig. 1

Flow diagram of the selection procedure for the inclusion of studies in the meta-analysis

Fig. 2

Summary of the QUADAS-2 evaluation performed on 56 articles. Proportions of Low risk of bias (Yes), Unclear and High risk of bias (No) are shown for A) key questions on applicability and bias and B) most important signalling questions

Fig. 3

Forest plot of A) sensitivities and B) specificities of the most important model from each study. The respective values and their confidence intervals can be seen on the right side of each plot

Fig. 4

SROCs of the bivariate models. A SROC of all reported models. Points with the same colour in the graph represent models which come from the same study. B SROC of the most important model from each study

Fig. 5

The calculated influence analysis was represented in Cook's distance units. A Influence analysis of most important models from each study. B Influence analysis of all reported models where the points with the same colour represent models which come from the same study

Fig. 6

Publication bias was performed on all reported models. Points with the same colour in the graph represent models which come from the same study. The cluster of grey points on the left-hand side of the graph represents the missing models which would be required in order not to have a publication bias

Fig. 7

SROCs of the subgroup bivariate models based on all reported models. A) Plasma vs Serum B) Single vs Multiple panel miRNAs C) Endogenous v Exogenous normalizer D) With vs Without stage III and stage IV cases

Fig. 8

Comparison of diagnostic performance of models to their imbalance of proportions of A) cases to controls or B) predicted positive to predicted negative screens, represented by a colour which corresponds to one of the three imbalance of proportions cut-point groups. Diagnostic performance means (with the confidence intervals) of the three ratio groups are represented by diamonds

Fig. 9

Preference estimates based on log (sensitivity/specificity) for all reported models using A) alpha for minimum Q and B) relative perceived cost of misdiagnosis (c₁). Points with the same colour in the graph represent models which come from the same study