Abstract 4361627: Circulating Long Non-Coding RNAs have Diagnostic and Risk Stratification Value with Mechanistic Roles in Heart Failure: a Meta-Analysis
Bibliographic record
Abstract
Background: Long non-coding RNAs (lncRNAs) are emerging as a promising RNA class with clinical relevance in heart failure (HF). These RNA transcripts are over 200 nucleotides long and regulate gene expression at multiple levels. While prior studies suggest circulating lncRNAs have clinical and diagnostic potential, comprehensive statistical analyses are lacking. This meta-analysis evaluates the diagnostic, risk stratification, and mechanistic roles of circulating lncRNAs in HF. Methods: MEDLINE and EMBASE databases were searched up to March 1 2025 for studies assessing circulating lncRNAs in adult patients with HF. Exclusion criteria included non-human, non-HF, pediatric, and non-English studies as well as reviews, editorials, letters, commentaries, and abstracts. Diagnostic accuracy analyses were performed using a bivariate random-effects model in STATA 19. Correlation analysis was performed using a random effects model in R 4.5.0. Pathway enrichment and protein-protein interaction analyses were performed using RNAenrich and Cytoscape 3.10.3. Heterogeneity was evaluated via I 2 . Results: After screening 955 titles, 34 studies involving 49 unique lncRNAs were included. Of these lncRNAs, 29 were significantly upregulated and 9 significantly downregulated (p < 0.05), while 5 showed no difference and 6 had unclear findings in HF. Diagnostic meta-analysis (8 studies, n = 1245) yielded a pooled sensitivity of 0.82 (95% CI: 0.68 – 0.91), specificity of 0.92 (95% CI: 0.85 – 0.96), and diagnostic odds ratio of 54 (95% CI: 26 – 112). Area under the curve of the summary receiver operating characteristic curve was 0.94 (95% CI: 0.92 – 0.96). Correlation analysis (5 studies, n = 593) showed a significant negative correlation between lncRNA levels and LVEF (r = -0.49, 95% CI: -0.58 – -0.39, p < 0.0001). Pathway enrichment analysis highlighted pathways involving growth factor receptors and second messengers, interleukins, and IL-4 and IL-13 in particular as important lncRNA-related pathways in HF. Tumour protein p53 (TP53), Suppressor of Mothers Against Decapentaplegic 4 (SMAD4), and specificity protein 1 (SP1) were identified as hub genes in these pathways. Conclusion: Circulating lncRNAs can differentiate HF from non-HF patients and correlate with cardiac function, aiding diagnosis and risk stratification. We also demonstrated potential pathophysiological roles of lncRNAs in HF involving growth factor receptors, IL-4, and IL-13.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.072 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".