Differential role of gene hypermethylation in adenocarcinomas, squamous cell carcinomas and cervical intraepithelial lesions of the uterine cervix
Bibliographic record
Abstract
Cervical cancer is the third most common cancer in women worldwide. The hypermethylation of P16, TSLC-1 and TSP-1 genes was analyzed in squamous cell carcinomas (SCC), cervical intraepithelial lesions (CIN) and adenocarcinomas (ADC) of the uterine cervix (total 181 lesions). Additionally human papillomavirus (HPV) type, EPB41L3, RASSF1 and RASSF2 hypermethylation were tested in ADC and the results were compared with those obtained previously by our group in SCC. P16, TSLC-1 and TSP-1 hypermethylation was more frequent in SCCs than in CINs. These percentages and the corresponding ones for EPB41L3, RASSF1 and RASSF2 genes were also higher in SCCs than in ADCs, except for P16. The presence of HPV in ADCs was lower than reported previously in SCC and CIN. Patients with RASSF1A hypermethylation showed significantly longer disease-free survival (P = 0.015) and overall survival periods (P = 0.009) in ADC patients. To our knowledge, this is the first description of the EPB41L3 and RASSF2 hypermethylation in ADCs. These results suggest that the involvement of DNA hypermethylation in cervical cancer varies depending on the histological type, which might contribute to explaining the different prognosis of patients with these types of tumors.
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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".