Lack of Association between miR-605 rs2043556 Polymorphism and Overall Cancer Risk: A Meta-analysis of Case-control Studies
Bibliographic record
Abstract
Growing evidence propose an association between miRNA polymorphisms and cancer susceptibility. This study aimed to examine the impact of miR-605 rs2043556 polymorphism on cancer risk through a meta-analysis based on 3198 cancer cases and 4943 controls. Eligible studies were retrieved by searching Web of Science, PubMed, Scopus, and Google Scholar databases up to August 27, 2018. The pooled Odds Ratios (ORs) with 95% Confidence Intervals (CIs) were calculated using a random-effect model to estimate the strength of association between rs2043556 variant of miR-605 and cancer risk. Overall, no significant association was found between miR-605 rs2043556 polymorphism and cancer risk in heterozygous codominant (OR=0.93, 95% CI=0.76-1.13, p=0.44, AG vs. AA), homozygous codominant (OR=1.01, 95%CI=0.78-1.30, p=0.94, GG vs. AA), dominant (OR=0.95, 95% CI=0.79-1.13, p=0.55, AG+GG vs. AA), recessive (OR=1.07, 95%CI=0.84-1.38, p=0.57, GG vs. AG+AA), overdominant (OR=0.93, 95% CI=0.76-1.12, p=0.43, AG vs. GG+AA), and allele (OR=0.98, 95% CI=0.87-1.10, p=0.73, G vs. A) genetic models tested. Stratified analysis by cancer type revealed that the rs2043556 variant was not associated with digestive tract cancer, breast cancer, gastric cancer as well as lung cancer. Taken together, the findings of this meta-analysis did not support an association between miR-605 rs2043556 polymorphism and cancer susceptibility.
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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.035 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".