Structural patterns and transcriptional effects of integrated Epstein-Barr virus revealed by long-read sequencing and RNA-sequencing in Nasopharyngeal Carcinoma
Bibliographic record
Abstract
Integration of Epstein-Barr virus (EBV) DNA into the human genome is a critical event in nasopharyngeal carcinogenesis. Here, we comprehensively characterize large-scale virus-human integration events in three EBV-positive nasopharyngeal carcinoma (NPC) cell lines and one patient NPC tumor using Nanopore long-read sequencing technology. We identified four distinct integration types, with Type A being particularly prevalent, characterized by the integration of a single fragment of the EBV genome followed by human DNA. Our findings reveal the involvement of multiple integration events in inducing inter-chromosomal translocations, leading to significant genomic disruption through chromosomal rearrangements. Additionally, we explore the relationship between EBV integration sites and structural variations, further supporting the role of EBV integration in driving genomic instability. By integrating RNA-seq data, we demonstrate the potential for EBV integration to disrupt gene expression, highlighting several integration sites within cancer-associated genes such as CD96, ARHGAP27, ASH1L, KDM3B, ZMYM2, and PIK3C2A. Notably, EBV-human fusion events were prevalent in EBV-associated NPCs, including intriguing fusion transcripts such as LRRC8C-RPMS1 and LINC00486-RPMS1, which provide further evidence of the oncogenic potential of EBV integration. Taken together, this study uncovers EBV integration patterns in Nasopharyngeal carcinogenesis using long-read sequencing technology.
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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 | 0.000 | 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 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".