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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.041 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".