Prognostic role of miR-190, miR-221, and miR-381 in breast cancer: a systematic review and meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: Breast cancer is the most frequently diagnosed malignancy in women and a leading cause of cancer-related mortality. Conventional prognostic tools may not fully capture disease outcomes. MicroRNA (miR) expression has emerged as a potential prognostic factor, though findings remain inconsistent. This systematic review and meta-analysis assess the prognostic role of miR-190, miR-221, and miR-381 in predicting overall survival (OS) among breast cancer patients. MATERIALS AND METHODS: A comprehensive literature search in PubMed, Embase, and Scopus identified relevant studies. Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated to assess the relationship between miR expression and OS. Subgroup analyses were conducted to explore potential sources of heterogeneity. RESULTS: Four studies on miR-221, four on miR-190, and three on miR-381 met inclusion criteria. High miR-190 expression was significantly associated with improved OS (HR: 0.63; 95% CI: 0.47-0.84), as was miR-381 (HR: 0.64; 95% CI: 0.52-0.79). No significant association was found between miR-221 expression and OS (HR: 1.12; 95% CI: 0.86-1.46). Subgroup analysis reinforced these findings, and Newcastle-Ottawa scale assessment indicated low publication bias in 10 out of 11. CONCLUSIONS: Elevated miR-190 and miR-381 levels are associated with improved OS in breast cancer, whereas the prognostic role of miR-221 remains unclear. These findings underscore the potential of miR-190 and miR-381 as prognostic biomarkers. Graphical Abstract: https://www.europeanreview.org/wp/wp-content/uploads/Graphical-Abstract-18-scaled.jpg.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".