Tumour cell blebbing and extracellular vesicle shedding: key role of matrikines and ribosomal protein SA
Bibliographic record
Abstract
BACKGROUND: Carcinogenesis occurs in elastin-rich tissues and leads to local inflammation and elastolytic proteinase release. This contributes to bioactive matrix fragment (Matrikine) accumulation like elastin degradation products (EDP) stimulating tumour cell invasive and metastatic properties. We previously demonstrate that EDPs exert protumoural activities through Hsp90 secretion to stabilised extracellular proteinases. METHODS: EDP influence on cancer cell blebbing and extracellular vesicle shedding were examined with a videomicroscope coupled with confocal Yokogawa spinning disk, by transmission electron microscopy, scanning electron microscopy and confocal microscopy. The ribosomal protein SA (RPSA) elastin receptor was identified after affinity chromatography by western blotting and cell immunolocalisation. mRNA expression was studied using real-time PCR. SiRNA were used to confirm the essential role of RPSA. RESULTS: We demonstrate that extracellular matrix degradation products like EDPs induce tumour amoeboid phenotype with cell membrane blebbing and shedding of extracellular vesicle containing Hsp90 and proteinases in the extracellular space. EDPs influence intracellular calcium influx and cytoskeleton reorganisation. Among matrikines, VGVAPG and AGVPGLGVG peptides reproduced EDP effects through RPSA binding. CONCLUSIONS: Our data suggests that matrikines induce cancer cell blebbing and extracellular vesicle release through RPSA binding, favouring dissemination, cell-to-cell communication and growth of cancer cells in metastatic sites.
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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".