Association of Elevated E6 Oncoprotein With Grade of Cervical Neoplasia Using PDZ Interaction-Mediated Precipitation of E6
Bibliographic record
Abstract
OBJECTIVE: To determine the expression of human papillomavirus (HPV) type 16 E6 oncoprotein in cervical specimens of women with and without cervical intraepithelial neoplasia (CIN). MATERIALS AND METHODS: Cervical specimens from 2,530 unscreened women aged 30 to 54 years from Shanxi, China, were obtained. All women were assessed by liquid-based cytology, high-risk HPV DNA tests, and colposcopy with directed biopsy and endocervical curettage as necessary. Women with abnormal cytologic results or positive HPV DNA results were recalled for colposcopy, 4-quadrant cervical biopsies, and endocervical curettage. Women with biopsy-proven CIN and cancer and a convenience sample of HC2-positive, disease-negative women were tested for the presence of HPV-16 infection via HPV-16 E6 DNA-specific polymerase chain reaction. A PDZ interaction-mediated E6 oncoprotein precipitation method followed by E6-specific Western blot was performed on specimens from women with HPV-16 infections. Associations between elevated expression of E6 oncoprotein and CIN 2 and 3 were determined using logistic regression and a reference category of CIN 1 and disease-negative. RESULTS: A significant trend for the detection of HPV-16 E6 oncoprotein in specimen of women with proven HPV-16 infection was determined: 0% (0/12), 12.5% (1/8), 36.4% (4/11), and 42.9% (3/7) of those with negative findings, CIN 1, 2, and 3, respectively (p = .01). Compared with the category combining negative findings and CIN 1, detection of E6 oncoprotein was associated with CIN 2 (odds ratio = 10.9, p = .05) and CIN 3 (odds ratio = 14.3, p = .04). CONCLUSIONS: There is a significant association between elevated expression of E6 oncoprotein and grade of CIN. This finding seems consistent with the role played by E6 oncoprotein in carcinogenesis.
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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 | 0.000 |
| 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) | 0.001 | 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".