Comprehensive Assessment of the Relationship Between MicroRNA-124 and the Prognostic Significance of Cancer
Bibliographic record
Abstract
Background: Numerous studies have demonstrated the presence of microRNA-124 abnormalities involving gene expression, methylation, and single nucleotide polymorphism (SNP) in multiple and diverse cancers，but the prognostic value of these abnormalities in cancer remains inconclusive. Objective: The aim of this study is to determine the prognostic value of miR-124 in cancer. Methods: We scrutinized the electronic databases and estimate the association between miR-124 expression, methylation and SNPs and prognosis in cancers. The pooled hazard ratios (HRs) with 95% confidence intervals (CIs) for overall survival (OS), and disease-free survival (DFS)/recurrence-free survival (RFS)/progression-free survival (PFS) were calculated to estimate the effects of miR-124 expression, methylation and SNPs on cancer prognosis. The Quality In Prognosis Studies (QUIPS) and Newcastle-Ottawa Scale (NOS) were utilized to assess the quality of included studies. Results: A total of 20 studies involving 3574 participants were analyzed in evidence synthesis. Our findings showed that the low expression of miR-124 was significantly associated with poor OS (HR=2.37, 95%CI: 1.91–2.94, P=0.00; HR=3.10, 95%CI: 2.04–4.70, P=0.00) and PFS/RFS (HR=2.21, 95%CI: 1.50–3.26, P=0.00; HR=2.12, 95%CI: 1.20–3.74, P=0.00). The hyper-methylation of miR-124 was associated with poor OS (HR=2.09, 95% CI: 1.48–2.95, □□=0.00) and PFS (HR=3.70, 95% CI: 1.72–7.97, □□=0.00) (Table 3). The patients carrying with Allele C of miR-124 rs5315649 had a worse OS (HR=1.50, 95% CI: 1.09–2.07, □□=0.00) and PFS (HR=1.67, 95% CI: 1.20–2.33, □□=0.00) than the carriers with Allele G. Conclusion: The low expression and hyper-methylation of miR-124 was strongly associated with poor prognosis, and genetic variations of miR-124 rs531564 affected prognosis in cancer patients.
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 | 0.038 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".