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 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 | 0.001 |
| 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".