Human leukocyte antigen G polymorphism is associated with an increased risk of invasive cancer of the uterine cervix
Bibliographic record
Abstract
Human leukocyte antigen (HLA)-G acts as negative regulator of the immune responses and its expression in tumor cells may enable them to escape immunosurveillance. The purpose of this study was to investigate the influence of HLA-G polymorphism on risk of high-grade cervical intraepithelial neoplasia (HG-CIN) and cervical cancer in a Canadian population. The authors have analyzed 1,372 women from participants recruited between 2001 and 2009 in the ongoing Biomarkers of Cervical Cancer Risk case-control study. A total of 539 women with histologically confirmed HG-CIN and invasive cancer formed the case series, and 833 women with normal cytology served as controls. Cervical specimens were tested for human papillomavirus (HPV) DNA using the MY09/11 PCR protocol and HLA-G alleles where determined using a direct DNA sequencing procedures. HLA-G polymorphisms were not associated with HG-CIN or HPV infection. However, the risk for invasive cancer was significantly increased with the homozygous genotypes HLA-G*01:01:02 [odds ratio (OR) = 3.52, 95% confidence interval (CI): 1.43-8.61, p = 0.006], -G*01:06 (OR = 19.1, 95% CI: 2.29-159, p = 0.005) and -G* 3'UTR 14-bp insertion (OR = 2.17, 95% CI: 1.10-4.27, p = 0.020), whereas, the heterozygotic form of the G*01:01:01 wild-type allele was significantly associated with a reduced risk of invasive cancer (OR = 0.31, 95% CI: 0.16-0.59, p < 0.0001) after adjusting for age, HPV infection and ethnicity. These associations were also observed with progression of disease from HG-CIN to invasive cancer among HPV-positive women. These results suggest that HLA-G polymorphism is an independent risk factor for the development of invasive 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".