Polymorphisms in miRNA genes play roles in the initiation and development of cervical cancer
Bibliographic record
Abstract
MicroRNA deregulation is crucial for cancer development. Studies showed that polymorphisms in miRNA genes could affect miRNA expression, which might be associated with cancer development. In the current study, we investigated the association of seven single nucleotide polymorphisms (SNPs) in seven miRNA genes with the initiation and development of cervical cancer in a Chinese Han population. The SNPs of 358 cervical intraepithelial neoplasia (CIN) patients, 547 cervical cancer patients and 567 healthy individuals were genotyped using TaqMan assays. Moreover, we evaluated the association of the seven SNPs with the different stages of cervical cancer. Our results showed that rs4636297 in miR-126 was associated with susceptibility to CIN and cervical cancer (P=0.019 and 0.019, respectively) and that the T allele was associated with a higher risk of CIN (OR=1.334, 95% CI: 1.049-1.698) and cervical cancer (OR=1.296, 95% CI: 1.044-1.609). Similarly, rs11614913 in miR-125a was associated with CIN and cervical cancer (P=0.025 and 0.015, respectively), and the T allele might be the protective factor for CIN (OR=0.807, 95% CI: 0.669-0.974) and cervical cancer (OR=0.814, 95% CI: 0.689-0.961). Our results indicated that rs4636297 in miR-126 and rs11614913 in miR-196a2 play an important role only in the initiation of cervical cancer not in the development of CIN to cervical cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".