Association between Long Non-coding RNA POLR2E rs3787016 Polymorphism and Cancer Susceptibility: A Meta-analysis of 8725 Cancer Cases and 10710 Controls
Bibliographic record
Abstract
Objectives: Several studies have reported a correlation between the POLR2E rs3787016 polymorphism and cancer development, but findings are inconsistent. Therefore, we designed the current study to understand how rs3787016 polymorphism impacts cancer susceptibility. Methods: We searched the Scopus, Web of Science, and PubMed databases for studies related to the topic of interest published up to March 2019. A total of 11 relevant studies, encompassing 8,761 cancer cases and 10,534 controls, were retrieved and subject to quantitative analysis. The strength of the relationship was evaluated using the pooled odds ratios (ORs) with 95% confidence intervals (CIs). Results: Overall, the findings proposed a positive association between rs189037 polymorphism and susceptibility to cancer in homozygous (OR = 1.32, 95% CI = 1.11 - 1.57, P = 0.002, TT vs. CC), recessive (OR = 1.21, 95% CI = 1.06-1.39, P = 0.005, TT vs. CT + CC), and allele (OR = 1.12, 95% CI = 1.02-1.22, P = 0.021, T vs. C) genetic models. Stratified analysis showed that rs3787016 increased the risk of prostate and breast cancer. In addition, we found a significant association between the variant and increased cancer risk in Asian and Caucasian populations. Conclusions: In summary, the findings of the current meta-analysis suggest that the POLR2E rs3787016 polymorphism is an indicator of cancer susceptibility.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.029 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".