Evaluation of the pri-miR-34b/c rs4938723 polymorphism and its association with breast cancer risk
Bibliographic record
Abstract
MicroRNAs (miRNAs or miRs) are a family of small non-coding RNAs that function as oncogenes or tumor suppressor genes. Recent evidence suggests that the pri-miR-34b/c rs4938723 variant is associated with the development of cancer. At present, there is an inconsistent association between the single-nucleotide polymorphism in pri-miR-34b/c and cancer in the limited studies. The present study is a case-control investigation, with 263 breast cancer (BC) patients and 221 control women, which examined the potential association of the pri-miR-34b/c rs4938723 polymorphisms with BC susceptibility. The polymorphisms were genotyped by the polymerase chain reaction restriction fragment length polymorphism method. No significant association between the pri-miR-34b/c rs4938723 variant and BC was identified [TC vs. TT: Odds ratio (OR), 0.87; 95% confidence interval (CI), 0.60-1.26; P=0.506; CC vs. TT: OR, 1.22; 95% CI, 0.61-2.47; P=0.600; TC+CC vs. TT: OR, 0.91; 95% CI, 0.64-1.31; P=0.648; CC vs. TT+TC: OR, 1.32; 95% CI, 0.67-2.59; P=0.498; C vs. T: OR, 0.99; 95% CI, 0.75-1.31; P=0.986]. However, a significant association was observed between the pri-miR-34b/c rs4938723 genotypes and clinicopathological characteristics, such a grade, progesterone receptor and human epidermal growth factor receptor 2 status were observed (P<0.05). These findings suggest that the pri-miR-34b/c rs4938723 variant may not be a risk factor for the development of BC.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".