Contributions of the Epstein-Barr Virus EBNA1 Protein to Gastric Carcinoma
Bibliographic record
Abstract
Approximately 10% of gastric carcinomas (GC) are comprised of cells latently infected with Epstein-Barr virus (EBV); however, the mechanism by which EBV contributes to the development of this malignancy is unclear. We have investigated the cellular effects of the only EBV nuclear protein expressed in GC, EBNA1, focusing on promyelocytic leukemia (PML) nuclear bodies (NBs), which play important roles in apoptosis, p53 activation, and tumor suppression. AGS GC cells infected with EBV were found to contain fewer PML NBs and less PML protein than the parental EBV-negative AGS cells, and these levels were restored by silencing EBNA1. Conversely, EBNA1 expression was sufficient to induce the loss of PML NBs and proteins in AGS cells. Consistent with PML functions, EBNA1 expression decreased p53 activation and apoptosis in response to DNA damage and resulted in increased cell survival. In addition, EBNA1 mutants unable to bind CK2 kinase or ubiquitin-specific protease 7 had decreased ability to induce PML loss and to interfere with p53 activation. PML levels in EBV-positive and EBV-negative GC biopsy specimens were then compared by immunohistochemistry. Consistent with the results in the AGS cells, EBV-positive tumors had significantly lower PML levels than EBV-negative tumors. The results indicate that EBV infection of GC cells leads to loss of PML NBs through the action of EBNA1, resulting in impaired responses to DNA damage and promotion of cell survival. Therefore, PML disruption by EBNA1 is one mechanism by which EBV may contribute to the development of gastric cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".