Prognostic Significance of the Epstein-Barr Virus, p53, Bcl-2, and Survivin in Nasopharyngeal Cancer
Bibliographic record
Abstract
PURPOSE: Nasopharyngeal cancer (NPC) is a malignant epithelial carcinoma which is intimately associated with EBV. The latent presence of EBV affects the function of p53, Bcl-2, and survivin. We thus investigated the relationship between EBV status, p53, Bcl-2, and survivin in biopsy specimens from patients with primary NPC. EXPERIMENTAL DESIGN: Archival formalin-fixed, paraffin-embedded NPC biopsies were evaluated in 80 patients treated with curative radiation from a single institution. The presence of EBV was determined using EBER in situ hybridization, whereas p53, Bcl-2, and survivin were assessed using immunohistochemistry. RESULTS: The majority of NPC specimens in this patient cohort were EBER-positive (64 of 78, or 82%), which in turn, was significantly associated with ethnicity (P = 0.0007), and WHO subtype 2A/2B (P = 0.04). EBER-positive tumors were also associated with p53 (P = 0.002), Bcl-2 (P = 0.04), and nuclear survivin (P = 0.03) expression. Patients with EBER-positive NPC fared better, with a 10-year overall survival of 68% versus 48% for EBER-negative patients (P = 0.03). For nuclear survivin, patients with either low or high nuclear survivin fared worse than patients with intermediate survivin expression (P = 0.05), suggesting that there is an optimal proportion of survivin-expressing cells for best function and clinical outcome. CONCLUSIONS: With an extended median follow-up time of 11.4 years, EBV status remains a strong predictor for overall survival in NPC. EBV-positive NPC has strong molecular associations with p53, Bcl-2, and survivin expression. Furthermore, we provide clinical data revealing the potentially dual nature of survivin in predicting clinical outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".