Diagnostic and predictive value of liquid biopsy-derived exosome miR-21 for breast cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Studies have revealed that miR-21 is abnormally expressed in breast cancer patients, suggesting that miR-21 could be exploited as a possible diagnostic biomarker for clinical applications. In order to provide clinical evidence that is supported by research, we investigate the diagnostic utility of miR-21 in breast cancer in this study. From their inception to 23 January 2022, the PubMed, EMBASE, Web of Science, Cochrane Library, and Scopus databases were searched for all pertinent English literature. QUADAS-2 for literature quality assessment, GRADE for evidence grading. Statistical analyses were performed using the R 4.0.1 and Revman 5.3. The results were validated using Stata 15.1 software. Subgroup analysis was also performed according to the source of miR-21 and miR-21 combinations. Nine publications with 2048 patients were reviewed for inclusion. All of the included studies are of moderate-high quality. Meta-analysis was performed using a mixed-effects model. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), negative likelihood ratio (NLR) and positive likelihood ratio (PLR) were 0.91 [95% CI (0.86, 0.95)], 0.85 [95% CI (0.77, 0.91)], 56.62 [95% CI (21.00, 184.83)], 0.11 [95% CI (0.05, 0.18)] and 6.35 [95% CI (3.66, 11.16)], respectively. The GRADE classification for miR-21 was A, indicating a strong recommendation for breast cancer screening. The available evidence suggests that miR-21 has sufficient diagnostic value as a biomarker for breast cancer. Its diagnostic precision can be further improved by combining it with other miRNAs. Based on the GRADE review, miR-21 is strongly recommended for breast cancer screening.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.052 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".