Differences in the Tumor Microenvironment of EBV-Associated Gastric Cancers Revealed Using Single-Cell Transcriptome Analysis
Bibliographic record
Abstract
Epstein-Barr virus (EBV) is a gamma-herpesvirus associated with nearly 10% of gastric cancers (GCs). These EBV-associated GCs (EBVaGCs) are molecularly, histopathologically, and clinically distinct from EBV-negative GCs (EBVnGCs). While viral genes in EBVaGCs contribute to the carcinogenesis process, viral proteins also represent foreign antigens that could trigger enhanced immune responses compared to EBVnGCs. Despite prior investigations of the EBVaGC tumor microenvironment (TME), the cellular composition has not been thoroughly explored. In this study, cellular subpopulations overrepresented in EBVaGCs were identified and molecularly characterized. Genes consistently expressed across both bulk tumor and single-cell RNA sequencing data were highlighted, with the expression across the identified cellular subpopulations analyzed. As expected, based on existing histopathological analysis, EBVaGC is characterized by abundant lymphocytic infiltration of the stroma. Our molecular analysis identified three unique immune cell subpopulations in EBVaGC: T and B cells expressing high levels of proliferation markers and B cells expressing T cell features. The proliferating T cell cluster also expressed markers of follicular T helper cells. Overall, EBVaGC also exhibited unique features indicative of a higher inflammatory response. These substantial differences within the TME suggest that further detailed exploration of the cellular composition of EBVaGCs is needed, which may identify cellular subpopulations and phenotypes associated with patient outcomes.
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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 | 0.000 | 0.000 |
| 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".