ReviewFrontiers in genetics2024
Diagnostic efficacy of long non-coding RNAs in multiple sclerosis: a systematic review and meta-analysis.
Review in Frontiers in genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- The Prospective Regulatory Functions of lncRNAs and Their ceRNA Networks in the Development of Motor Neurons and Associated Diseases.Biomolecules · 2026Review
- Artificial intelligence in ECG-based diagnosis of low left ventricular ejection fraction: a systematic review and meta-analysis.Biomedical engineering letters · 2025Review
- Tumor-Educated Platelets lncRNA-STARD4-AS1 and ELOA-AS1 as Potential Novel Biomarkers for the Early Diagnosis of Non-Small Cell Lung Cancer.Cancer management and research · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Currently, an increasing body of research suggests that blood-based long non-coding RNAs (lncRNAs) could serve as biomarkers for diagnosing multiple sclerosis (MS). This meta-analysis evaluates the diagnostic capabilities of selected lncRNAs in distinguishing individuals with MS from healthy controls and in differentiating between the relapsing and remitting phases of the disease. Methods: We conducted comprehensive searches across seven databases in both Chinese and English to identify relevant studies, applying stringent inclusion and exclusion criteria. The quality of the selected references was rigorously assessed using the QUADAS-2 tool. The analysis involved calculating summarized sensitivity (SSEN), specificity (SSPE), positive likelihood ratio (SPLR), negative likelihood ratio (SNLR), and diagnostic odds ratio (DOR) with 95% confidence intervals (CIs). Accuracy was assessed using summary receiver operating characteristic (SROC) curves. Results: Thirteen high-quality studies were selected for inclusion in the meta-analysis. Our meta-analysis assessed the combined diagnostic performance of lncRNAs in distinguishing MS patients from healthy controls. We found a SSEN of 0.81 (95% CI: 0.74-0.87), SSPE of 0.84 (95% CI: 0.78-0.89), SPLR of 5.14 (95% CI: 3.63-7.28), SNLR of 0.22 (95% CI: 0.16-0.31), and DOR of 23.17 (95% CI: 14.07-38.17), with an AUC of 0.90 (95% CI: 0.87-0.92). For differentiating between relapsing and remitting MS, the results showed a SSEN of 0.79 (95% CI: 0.71-0.85), SSPE of 0.76 (95% CI: 0.64-0.85), SPLR of 3.34 (95% CI: 2.09-5.33), SNLR of 0.28 (95% CI: 0.19-0.40), and DOR of 12.09 (95% CI: 5.70-25.68), with an AUC of 0.84 (95% CI: 0.81-0.87). Conclusion: This analysis underscores the significant role of lncRNAs as biomarkers in MS diagnosis and differentiation between its relapsing and remitting forms.
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