Causal role of MiRNAs in chronic rhinosinusitis: mendelian randomization and validation study
Bibliographic record
Abstract
BACKGROUND: Despite significant advances in understanding the epigenetic landscape of chronic rhinosinusitis (CRS), the specific microRNAs (miRNAs) with a causal role in CRS pathogenesis remain unclear. OBJECTIVE: This study aims to identify miRNAs that causally contribute to CRS and to elucidate their clinical relevance and underlying molecular mechanisms. METHODS: We employed Mendelian randomization (MR) analysis, leveraging mirQTLs as exposure variables and two independent CRS datasets as outcomes, to identify miRNAs causally linked to CRS. Robustness of the findings was ensured through multiple sensitivity analyses. The expression levels of identified CRS-associated miRNAs were validated using qRT-PCR, and their diagnostic potential was assessed through ROC curve analysis. Target genes and potential pathways regulated by the causal miRNAs were predicted via MiRNet and enrichment analyses, followed by experimental validation using western blotting and immunohistochemistry. RESULTS: MiR-130a-3p and miR-196b-5p were significantly associated with an increased risk of CRS, while miR-339-3p was associated with a decreased risk. These associations were confirmed by qRT-PCR, and no evidence of pleiotropy or heterogeneity was observed. ROC analysis revealed diagnostic potential for these miRNAs in CRS. Enrichment and experimental analyses suggested that the MAPK and PI3K-AKT pathways are predominantly activated by the target genes of the positively and negatively associated miRNAs, respectively. CONCLUSIONS: MiR-130a-3p and miR-196b-5p are positively associated with CRS risk, whereas miR-339-3p is protective. These miRNAs represent promising diagnostic biomarkers and therapeutic targets for CRS. The MAPK and PI3K-AKT pathways likely mediate the effects of these causal miRNAs, offering further insight into the molecular mechanisms underlying CRS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".