Microvesicles derived from activated platelets induce metastasis and angiogenesis in lung cancer
Bibliographic record
Abstract
The role of platelets in tumor progression and metastasis has been recognized but the mechanism of their action remains unclear. Five human lung cancer cell lines (A549, CRL 2066, CRL 2062, HTB 183, HTB 177) and a murine Lewis lung carcinoma (LCC) cell line (for an in vivo model of metastasis) were used to investigate how platelet-derived microvesicles (PMV), which are circular fragments shed from the surface membranes of activated platelets, and exosomes released from platelet alpha-granules, could contribute to metastatic spread. We found that PMV transferred the platelet-derived integrin CD41 to most of the lung cancer cell lines tested and stimulated the phosphorylation of mitogen-activated protein kinase p42/44 and serine/threonine kinase as well as the expression of membrane type 1-matrix metalloproteinase (MT1-MMP). PMV chemoattracted 4 of the 5 cell lines, with the highly metastatic A549 cells exhibiting the strongest response. In A549 cells, PMV were shown to stimulate proliferation, upregulate cyclin D2 expression and increase trans-Matrigel chemoinvasion. Furthermore, in these cells, PMV stimulated mRNA expression for angiogenic factors such as MMP-9, vascular endothelial growth factor, interleukin-8 and hepatocyte growth factor, as well as adhesion to fibrinogen and human umbilical vein endothelial cells. Intravenous injection of murine PMV-covered LLC cells into syngeneic mice resulted in significantly more metastatic foci in their lungs and LLC cells in bone marrow than in control animals injected with LCC cells not covered with PMV. Based on these findings, we suggest that PMV play an important role in tumor progression/metastasis and angiogenesis in lung 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) | 0.001 | 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".