mirSNPs as Potential Colorectal Cancer Biomarkers: A Systematic Review
Bibliographic record
Abstract
Colorectal cancer (CRC) is the third most common neoplasm in the world and the second with the highest mortality rate. Single nucleotide polymorphisms (SNPs) in microRNA (miRNA) genes known as mirSNPs may be related to dysregulated miRNA expression in several neoplasms. This systematic review aims to investigate studies that investigate SNPs located in regions of miRNA genes that influence their expression and are associated with CRC, as well as their potential as biomarkers for the disease, based on the available literature. For this, searches were performed in public databases, including MEDLINE/PubMed, Embase, Web of Science, and Scopus. The rigorous review of the PRISMA 2020 guidelines and the methodological quality of these studies was assessed using the Newcastle-Ottawa scale and the Mixed Methods Assessment Tool. Of the 175 studies identified, 26 were considered eligible: 18 of them highlighted mirSNPs as potential biomarkers of risk and prognosis for CRC; 4 studies suggested a protective role; 1 study linked mirSNPs to treatment; 3 studies found no relevant evidence. These results highlight the importance of conducting further research on the topic, given the potential of these biomarkers to contribute to risk assessment, prognosis, and the development of therapeutic strategies for patients with CRC.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".