Physical Status of Human Papillomavirus Integration in Cervical Cancer Is Associated with Treatment Outcome of the Patients Treated with Radiotherapy
Bibliographic record
Abstract
Integration of human papillomavirus (HPV) DNA into the host genome is a critical aetiological event in the progression from normal cervix to intraepithelial neoplasm, and finally to invasive cervical cancer. However, there has been little work on how HPV integration status relates to treatment outcome for cervical carcinomas. In the current study, HPV E2 and E6 gene copy numbers were measured in 111 cervical cancer tissues using real-time QPCR. Integration patterns were divided into four groups: single copy-integrated with episomal components (group 1), single copy-integrated without episomal components (group 2), multicopy tandem repetition-integrated (group 3), and low HPV (group 4) groups. A relapse-predicting model was constructed using multivariable Cox proportional hazards model to classify patients into different risk groups for disease-free survival (DFS). The model was internally validated using bootstrap resampling. Oligonucleotide microarray analysis was performed to evaluate gene expression patterns in relation to the different integration groups. DFS rate was inferior in the order of the patients in group 4, group 2/3, and group 1. Multivariate analysis showed that histologic grade, clinical stage group, and integration pattern were significant prognostic factors for poor DFS. The current prognostic model accurately predicted the risk of relapse, with an area under the receiver operating characteristic curve (AUC) of 0.74 (bootstrap corrected, 0.71). In conclusion, these data suggest that HPV integration pattern is a potent prognostic factor for tailored treatment of 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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".