High-risk human papillomaviruses and Epstein–Barr virus in breast cancer in Lebanese women and their association with tumor grade: a molecular and tissue microarray study
Bibliographic record
Abstract
BACKGROUND: High-risk human papillomaviruses (HPVs) are present and can cooperate with Epstein-Barr virus (EBV) to initiate and/or enhance the progression of several types of human carcinomas including cervical as well as head and neck; in parallel, it has been recently pointed out that these oncoviruses can be detected in human breast cancers. Thus, we herein explored the presence/co-presence of high-risk HPVs and EBV in breast cancer in Lebanese women. METHODS: A cohort of 102 breast cancer samples and 14 normal breast tissues were assessed for the presence of HPVs and EBV. Polymerase chain reaction (PCR) and immunohistochemistry (IHC) analysis in addition to tissue microarray (TMA) platform were used in this study. RESULTS: We found the presence of HPV in 66/102 (65%) of our samples, while EBV is present in 41/102 (40%) of the cohort. Additionally, our data showed that high-risk HPV types (52, 35, 58, 45, 16 and 51) are the most frequent in breast cancer in Lebanese women. Meanwhile, we report that high-risk HPVs and EBV are co-present in 30/102 (29%) of the samples; more significantly, our results indicate that their co-presence is associated with tumor grade (p = 0.03). CONCLUSION: Our data revealed that HPVs and EBV are present/co-present in human breast cancer where they may play an important role in its development and/or progression; thus, we believe that further investigations are essential to confirm and elucidate the presence/co-presence of these oncoviruses and the underlying mechanisms of their interaction in breast 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".