Met receptor tyrosine kinase transactivation is involved in proteinase-activated receptor-2-mediated hepatocellular carcinoma cell invasion
Bibliographic record
Abstract
The expression of proteinase-activated receptor (PAR)(2) in human hepatocellular carcinoma (HCC) was established by reverse transcription-polymerase chain reaction, confocal immunofluorescence and electron microscopy in permanent cell lines, primary HCC cell cultures and HCC tumor tissue. Stimulation of HCC cells with trypsin and the PAR(2)-selective activating peptide, 2-furoyl-LIGRLO-NH(2), increased cell invasion across Matrigel. Both effects were blocked by a PAR(2)-selective pepducin antagonist peptide (pal-PAR(2)) and by PAR(2) silencing with specific small interfering RNA (siRNA). PAR(2)-initiated HCC cell invasion was also blocked by inhibiting the hepatocyte growth factor receptor (Met receptor tyrosine kinase) with the receptor-targeted kinase inhibitors, SU 11274 and PHA 665752, or by downregulation of Met with specific siRNA. The involvement of Met in PAR(2)-mediated HCC invasive signaling was further supported by the finding that treatment of HCC cells with trypsin or the PAR(2)-selective agonist peptide, 2-furoyl-LIGRLO-NH(2), stimulated Met activation-phosphorylation. In addition, Met-dependent stimulation of p42/p44 mitogen-activated protein Kinases was found to be critical for the PAR(2)-Met receptor tyrosine kinase-invasive signaling axis in HCC cells. Our study establishes an important link between the PAR(2) and Met receptor tyrosine kinase signaling in promoting HCC cell 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".