Studies on the role of extracellular vesicles in horizontal transfer of oncogenes in cancer
Bibliographic record
Abstract
Extracellular vesicles (EVs) are spherical or cup-shaped membrane structures that originate from 'donor' cells and are released into their pericellular space, travelling considerable distances in the interstitial space until they undergo uptake, fusion or interaction with a range of 'acceptor' cells.EVs have been documented to harbour cancer-associated molecules, including oncogenic proteins, transcripts and genomic sequences containing mutant oncogenes, and to participate in their intercellular trafficking that results in horizontal transformation.Here, we address the questions about EV isolation techniques, their DNA cargo and barriers to EV-mediated transformation.We show that while ultracentrifugation approach is sufficient to eliminate the rare cancer cells that may enter EV preparations, certain highly transformed cells enter EV preparations having survived the extreme centrifugal force.We also show that oncogenic H-ras drives vesiculation of intestinal epithelial cells resulting in shedding of small EVs that contain double-stranded chromatin-associated DNA sequences, which are representative of the entire host genome and includes the transforming H-ras oncogene.In addition, we demonstrate that EV formation is affected antithetically by both oncogenes and tumour suppressors, and is further modified by hypoxia and calcium signalling pathways within human glioma cells.Lastly, we reveal that selective, transient nature of EV uptake and EV-induced toxicity are natural barriers x 8.2.Future Directions -Unanswered Questions………………………..…………...........
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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".