von Hippel‐Lindau tumor suppressor protein stimulation by thrombin involves RhoA activation
Bibliographic record
Abstract
Inactivation of the von Hippel-Lindau (VHL) tumor suppressor gene is associated with the development of vascular tumors including renal cell carcinoma. Aside from the role played by the VHL protein (pVHL) in negative regulation of hypoxia-inducible factor, 41F-1alpha, pVHL also takes part in cytoskeletal organization. Thrombin is a serine protease involved in angiogenesis and in cancer progression and its action is mediated by the protease-activated receptors (PARs). In several cell types, thrombin induces reorganization of the cytoskeleton along with RhoA activation. Thus, we conducted an investigation on the capacity of thrombin to regulate pVHL expression. Our results demonstrated that VHL mRNA and protein levels were increased by thrombin in cultured renal cancer cells. Cytoplasmic pVHL was redistributed to perinuclear regions and membrane fractions following thrombin treatments. Stimulation of Caki-1 cells with PAR1, PAR2 and PAR4 agonist peptides demonstrated that PAR1 was the receptor involved in thrombin-induced pVHL expression. Western blot analysis confirmed that these cells express PAR1 and that its expression was increased by thrombin. PAR1 activation by both thrombin and an agonist peptide stimulated renal cancer cell invasion through Matrigel. Interestingly, the upregulation of pVHL was dependent on RhoA because C3 exotoxin abolished pVHL induction. However, the pharmacological Rho kinase inhibitor, Y27632, did not influence pVHL expression in the presence of thrombin, suggesting that other RhoA effectors were involved in the process. Together, these results demonstrate that thrombin induces both pVHL expression via PAR1/RhoA activation as well as the stimulation of renal cancer cell invasion suggesting a role for thrombin in tumor invasion.
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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".