Extracellular Vesicles from Ocular Melanoma Have Pro-Fibrotic and Pro-Angiogenic Properties on the Tumor Microenvironment
Bibliographic record
Abstract
Uveal melanoma (UM) is the most common primary intraocular tumor and often spreads to the liver. Intercellular communication though extracellular vesicles (EVs) plays an important role in several oncogenic processes, including metastasis, therapeutic resistance, and immune escape. This study examines how EVs released by UM cells modify stellate and endothelial cells in the tumor microenvironment. The surface markers, and the concentration and size of EVs derived from UM cells or choroidal melanocytes were characterized by high-resolution flow cytometry, electron microscopy, and Western blotting. The selective biodistribution of EVs was studied in mice by fluorescence imaging. The activation/contractility of stellate cells and the tubular organization of endothelial cells after exposure to melanomic EVs were determined by traction force microscopy, collagen gel contraction, or endothelial tube formation assays. We showed that large EVs from UM cells and healthy melanocytes are heterogenous in size, as well as their expression of phosphatidylserine, tetraspanins, and Tsg101. Melanomic EVs mainly accumulated in the liver and lungs of mice. Hepatic stellate cells with internalized melanomic EVs had increased contractility, whereas EV-treated endothelial cells developed more capillary-like networks. Our study demonstrates that the transfer of EVs from UM cells leads to a pro-fibrotic and pro-angiogenic phenotype in hepatic stellate and endothelial cells.
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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".