Diagnostic Value of microRNA Signatures for Early and Non-Invasive Detection of Colorectal Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Colorectal cancer (CRC) continues to be a primary contributor to the global health burden, and early detection is vital for optimal outcomes. Standard detection techniques, including colonoscopy and fecal occult blood tests, have been confirmed effective yet their invasiveness and poor sensitivity are a limitation. MicroRNA (miRNA) are now recognized as stable non-invasive biomarkers with differential expression in cancerous tissues, but reports have been heterogeneous and studied under different settings. This study, completed based on PRISMA guidelines, was a systematic review of miRNA signature diagnostic accuracy to detect early CRC. Case-case control studies, cross-sectional, and cohort research published between 2014–2024 were identified through the Scopus, PubMed, and Cochrane databases. We extracted diagnostic metrics such as sensitivity and specificity while assessing bias with the ROBINS-e tool and the Newcastle-Ottawa Scale. Meta-analyses showed that miRNA panels have high diagnostic accuracy with a pooled sensitivity of 1.84 (95% CI: 1.48–2.19) and a pooled specificity of 1.43 (95% CI: 1.01–1.85). The accuracy of miRNA-139-3p was the highest among all panels. Meta-regression did not reveal any significant confounders, while publication bias was not detected. These results highlight the miRNA panels’ potential as non-invasive biomarkers in early CRC detection, providing a promising alternative to conventional screening methods, with miRNA-139-3p as the most diagnostically accurate biomarker.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".