Exosomes and microvesicles derived from activated platelets increase the metastatic potential of lung cancer cells
Bibliographic record
Abstract
1764 It has been recognized that platelets exacerbate tumor progression and metastasis but the possible role of circular fragments shed from the surface membranes of activated platelets (microvesicles) and of exosomes that are secreted as smaller fragments during degranulation of platelet α-granules has not been sufficiently investigated. As the level of platelet-derived microvesicles (PMV) increases in the peripheral blood of cancer patients during inflammation, thrombosis or platelet activation by tumor cells, we hypothesized that PMV could contribute to tumor progression/metastasis. We used five human lung cancer cell lines (CRL 2066, CRL 2062, A549, HTB 183, HTB 177), together with the murine Lewis Lung Carcinoma (LLC) cell line as an in vivo model of metastasis, and found that PMV not only transferred platelet-derived receptors to the surface of lung cancer cells but also stimulated them directly, modulating various aspects of their biology. These included stimulation of cell proliferation, chemotaxis, chemoinvasion across Matrigel and adhesion to fibrinogen and human endothelial cells, activation of phosphorylation of MAPK p42/44 and serine/threonine kinase AKT, as well as upregulation of mRNA expression for cyclin D2. Further, PMV upregulated secretion and expression of angiogenic factors such as VEGF, IL-8, HGF and matrix metalloproteinases (MMP-9 and MT1-MMP) in lung cancer A549 cells. Moreover, syngeneic animals injected with LLC cells covered with murine PMV had enhanced metastasis compared to animals injected with non-covered LLC cells. Based on these observations we conclude that PMV and exosomes take part in intercellular crosstalk and play a prominent role in modulating the metastatic potential of lung cancer cells. We have already obtained evidence that PMV operate similarly in other types of tumors including breast cancer.
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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) | 0.002 | 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".