Co-presence of human papillomaviruses and Epstein–Barr virus is linked with advanced tumor stage: a tissue microarray study in head and neck cancer patients
Bibliographic record
Abstract
BACKGROUND: Human papillomaviruses (HPVs) and Epstein-Barr virus (EBV), known oncoviruses, can be co-present and cooperate in the initiation and/or progression of human carcinomas, including head and neck. Based on this fact, we recently reported the prevalence of both HPVs and EBV in cervical and breast cancers. METHODS: We herein explore for the first time the co-prevalence of high-risk HPVs and EBV in 98 head and neck (HN) squamous cell carcinoma (SCC) tissues from Bosnian patients using polymerase chain reaction (PCR) and immunohistochemistry (IHC) analysis, as well as tissue microarray methodology. RESULTS: The majority of these cancer tissue cases were from the oral cavity (68%). We found that high-risk HPVs and EBV are co-present in 34.7% of the SCC samples; with a significant correlation between the various HPV types and EBV co-incidence (p = 0.03). Our data showed that 30.8% of oral SCCs are positive for E6 oncoprotein of high-risk HPVs and 44.6% are positive for LMP1 of EBV. The most commonly expressed HPVs in our HNSCC samples include HPV types 16, 18, 45 and 58. Additionally, 37.5% of oral SCCs are positive for both HPVs and EBV, with statistically significant association between high-risk HPV types and EBV (p < 0.05). More importantly, our data revealed that the co-presence of HPV and EBV is strongly correlated with advanced tumor stage (p = 0.035). CONCLUSION: In this study we show that HPV and EBV oncoviruses are co-present in HNSCC, particularly in oral cancer, where they can cooperate in the initiation and/or progression of this cancer. Thus, further studies are necessary to elucidate the mechanism of this cooperation.
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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 | 0.000 |
| 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 | 0.000 |
| 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".