Long Non-Coding RNAs as Diagnostic Biomarkers for Ischemic Stroke: A Systematic Review and Meta-Analysis
Abstract
Ischemic stroke is a serious cerebrovascular disease, highlighting the urgent need for reliable biomarkers for early diagnosis. Recent reports suggest that long non-coding RNAs (lncRNAs) can be potential biomarkers for ischemic stroke. Therefore, our study seeks to investigate the potential diagnostic value of lncRNAs for ischemic stroke by analyzing existing research. A comprehensive literature search was conducted across the PubMed, ScienceDirect, Wiley Online Library, and Web of Science databases for articles published up to July 10, 2024. Statistical analyses were performed using Stata 17.0 software to calculate pooled sensitivity, specificity, positive likelihood ratio (PLR), diagnostic odds ratio (DOR), negative likelihood ratio (NLR), and area under the curve (AUC). Heterogeneity was explored with the Cochran-Q test and the I2 statistical test, and publication bias was assessed with Deeks’ funnel plot. A total of 44 articles were included, involving 4302 ischemic stroke patients and 3725 healthy controls. Results demonstrated that lncRNAs H19, GAS5, PVT1, TUG1, and MALAT1 exhibited consistent trends across multiple studies. The pooled sensitivity of lncRNAs in the diagnosis of ischemic stroke was 79% (95% CI: 73–84%), specificity was 88% (95% CI: 77–94%), PLR was 6.63 (95% CI: 3.11–14.15), NLR was 0.23 (95% CI: 0.16–0.33), DOR was 28.5 (95% CI: 9.88–82.21), and AUC was 0.88 (95% CI: 0.85–0.90). Furthermore, the results of subgroup analysis indicated that lncRNA H19…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 4,302 participants or records.
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