<i>p53 Arg72Pro</i> Polymorphism, HPV Status and Initiation, Progression, and Development of Cervical Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Cervical cancer develops through progression from normal cervical epithelium through squamous intraepithelial lesions (SIL) to invasive cancer. Cervical cancer is associated with oncogenic human papillomavirus (HPV). The HPV E6 oncoprotein binds to the tumor suppressor gene product p53, promoting its degradation; the Arg allele of p53 Arg72Pro polymorphism binds more ardently with HPV E6 than the Pro variant. Here we evaluate the role of p53 Arg72Pro polymorphism and HPV status on the initiation, progression, and development of cervical cancer. A systematic review and meta-analysis were conducted. Events of interest were the initiation of neoplasia (SIL vs. normal), progression to invasive cancer (cervical cancer vs. SIL), and risk of invasive cancer (cervical cancer vs. normal) by HPV status. OR were extracted from individual studies and pooled using generic inverse variance and random effects modeling. Forty-nine studies were included. In individuals showing HPV positivity, there was a significantly higher odds of progression from SIL to cervical cancer with the p53 Arg allele [OR 1.37; 95% confidence intervals (CI), 1.15-1.62; P < 0.001]. This association was not seen in HPV-negative individuals. p53 Arg72Pro was not associated with the risk of cervical cancer or initiation of SIL in either HPV-positive or HPV-negative patient subsets. The Arg variant of p53 Arg72Pro is associated with progression of SIL to cervical cancer only in the presence of HPV positivity. There were no associations of this variant with overall risk or initiation of cancer in either HPV-positive or HPV-negative patients. Clin Cancer Res; 18(23); 6407-15.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.020 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".