Exosomal HIF1α supports invasive potential of nasopharyngeal carcinoma-associated LMP1-positive exosomes
Bibliographic record
Abstract
It has emerged recently that exosomes are potential carriers of pro-tumorigenic factors that participate in oncogenesis. However, whether oncogenic transcription factors are transduced by exosomes is unknown. Hypoxia-inducible factor-1α (HIF1α) transcriptionally regulates numerous key aspects of tumor development and progression by promoting a more aggressive tumor phenotype, characterized by increased proliferation and invasiveness coupled with neoangiogenesis. It has been shown that the principal oncoprotein of Epstein-Barr virus (EBV), latent membrane protein 1 (LMP1), drives oncogenic processes and tumor progression of the highly invasive EBV malignancy, nasopharyngeal carcinoma (NPC). We now demonstrate that endogenous HIF1α is detectable in exosomes and that LMP1 significantly increases levels of HIF1α in exosomes. HIF1 recovered from exosomes retains DNA-binding activity and is transcriptionally active in recipient cells after exosome uptake. We also show that treatment of EBV-negative cells with LMP1-exosomes increases migration and invasiveness of NP cell lines in functional assays, which correlates with the phenotype associated with epithelial-mesenchymal transition (EMT). In addition, we provide evidence that HIF1α itself participates in exosome-mediated pro-metastatic effects in recipient cells, as exosome-mediated delivery of active and inactive forms of HIF1α results in reciprocal changes in the expression of E- and N-cadherins associated with EMT. Further, immunohistochemical analysis of NPC tumor tissues revealed direct correlation between protein levels of LMP1 and of the endosome/exosome marker tetraspanin, CD63, which suggests an increase in exosome formation in this EBV-positive malignancy. We hypothesize that exosome-mediated transfer of functional pro-metastatic factors by LMP1-positive NPC cells to surrounding tumor cells promotes cancer progression.
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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) | 0.001 | 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".