Probing the infiltrating character of brain tumors: inhibition of RhoA/ROK‐mediated CD44 cell surface shedding from glioma cells by the green tea catechin EGCg
Bibliographic record
Abstract
Glioma cell-surface binding to hyaluronan (HA), a major constituent of the brain extracellular matrix (ECM) environment, is regulated through a complex membrane type-1 matrix metalloproteinase (MT1-MMP)/CD44/caveolin interaction that takes place at the leading edges of invading cells. In the present study, intracellular transduction pathways required for the HA-mediated recognition by infiltrating glioma cells in brain was investigated. We show that the overexpression of the GTPase RhoA up-regulated MT1-MMP expression and triggered CD44 shedding from the U-87 glioma cell surface. This potential implication in cerebral metastatic processes was also observed in cells overexpressing the full-length recombinant MT1-MMP, while the overexpression of a cytoplasmic domain truncated from of MT1-MMP failed to do so. This suggests that the cytoplasmic domain of MT1-MMP transduces intracellular signaling leading to RhoA-mediated CD44 shedding. Treatment of glioma cells with the Rho-kinase (ROK) inhibitor Y27632, or with EGCg, a green tea catechin with anti-MMP and anti-angiogenesis activities, antagonized both RhoA- and MT1-MMP-induced CD44 shedding. Conversely, overexpression of recombinant ROK stimulated CD44 release. Taken together, our results suggest that RhoA/ROK intracellular signaling regulates MT1-MMP-mediated CD44 recognition of HA. These molecular processes may partly explain the diffuse brain-infiltrating character of glioma cells within the surrounding parenchyma and thus be a target for new approaches to anti-tumor therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".