Association between miR‐34b/c rs4938723 polymorphism and risk of cancer: An updated meta‐analysis of 27 case‐control studies
Bibliographic record
Abstract
Several studies investigated the association between miR-34b/c rs4938723 polymorphism and the risk of several human cancers, but the findings remain inconclusive. To evaluate the impact of miR-34b/c rs4938723 on cancer risk, we performed a meta-analysis on all available studies including 12 361 cancer cases and 14 270 controls. Eligible studies were identified by searching PubMed, Web of Science, Scopus, and Google scholar databases. Pooled odds ratios with 95% confidence intervals were calculated in codominant, dominant, recessive, overdominant, and allele models to quantitatively estimate the association. The overall findings showed no significant association between miR-34b/c rs4938723 polymorphism and cancer risk in codominant, dominant, recessive, overdominant, and allele inheritance model. However, in stratified analysis by cancer types, the rs4938723 polymorphism significantly increased the risk of gastrointestinal cancer, hepatocellular carcinoma. In addition, the rs4938723 polymorphism was associated with decreased risk of esophageal squamous cell carcinoma, colorectal cancer, and acute lymphoblastic leukemia. The findings did not support an association between rs4938723 variant and digestive tract as well as gastric cancer. In summary, the findings of this meta-analysis indicated that the miR-34b/c rs4938723 polymorphism might be associated with some cancer development. Larger and well-designed studies are necessary to estimate this association in detail.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".