Long non-coding RNA POLR2E gene polymorphisms increased the risk of prostate cancer in a sample of the Iranian population
Bibliographic record
Abstract
The current study aimed to examine the impact of POLR2E rs1046040 and rs3787016 polymorphisms on prostate cancer (PCa) risk in a sample of southeast Iranian population. The present case-control study was performed on 178 patients with PCa and 180 benign prostatic hyperplasia (BPH). Genotyping of the variants was done by mismatch PCR-RFLP. The findings showed that the rs3787016 C > T variant significantly increased the risk of PCa in codominant (OR = 1.84, 95% CI = 1.12-3.03, P = 0.018, CT vs CC), dominant (OR = 1.88, 95% CI = 1.63-3.05, P = 0.011, CT + TT vas CC) and allele (OR = 1.77, 95% CI = 1.52-2.72, P = 0.010, T vs C) inheritance model. Regarding rs1046040 C > T polymorphism, the findings revealed that the CT genotype significantly increased the risk of PCa compared to the CC genotype (OR = 1.60, 95% CI = 1.03-2.49, P = 0.043). Furthermore, rs3787016 CT/rs1046040 CC as well as rs3787016 CT/rs1046040 CT increased the risk of PCa compared to the CC/CC genotype (p = 0.029 and p = 0.014, respectively). Haplotype analysis proposed that rs3787016 T/rs1046040 C significantly increased the risk of PCa compared to C/C (p = 0.037). No significant association was observed between POLR2E variants and clinicopathological characteristics of PCa patients. In conclusion, the findings propose that POLR2E variants may be a risk factor for susceptibility to PCa in a sample of Iranian population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".