A prostate secretory protein94‐derived synthetic peptide PCK3145 inhibits VEGF signalling in endothelial cells: Implication in tumor angiogenesis
Bibliographic record
Abstract
We have previously observed that the synthetic peptide corresponding to amino acids 31-45 (PCK3145) of PSP94 can reduce prostate tumor growth in vivo. Moreover, a recently concluded phase IIa clinical trial with patients with hormone refractory prostate cancer indicated that PCK3145 down-regulates the levels of plasma matrix metalloproteinase (MMP)-9, a MMP involved in metastasis and tumor angiogenesis. The purpose of our study was to investigate the molecular mechanisms of action of PCK3145 and whether this peptide could antagonize tumor neovascularization. We show that, in a syngeneic in vivo model of rat prostate cancer, the expression of endothelial cell (EC) specific CD31, a marker of tumor vessel density, was decreased by 43% in PCK3145-treated animals. In vitro, PCK3145 specifically antagonized in a dose-dependent manner the VEGF-induced ERK phosphorylation as well as the phosphorylation of the VEGFR-2 in cultured EC (HUVEC). These anti-VEGF effects were partly reproduced by pharmacological inhibitors such as PD98059 and PTK787, suggesting that PCK3145 inhibits the tyrosine kinase activity associated to VEGFR-2, which in turn prevents intracellular signalling through the MAPK cascade. Moreover, PCK3145 was also found to inhibit the PDGF-induced phosphorylation of PDGFR in smooth muscle cells. Finally, PCK3145 inhibited in vitro EC tubulogenesis and VEGF-induced MMP-2 secretion suggesting its potential implication as an antiangiogenic agent. Our study demonstrates that PCK3145 interferes with the tyrosine kinase activity associated with VEGF signalling axis in EC. The antiangiogenic properties of this peptide could be highly beneficial and exploited in novel antiangiogenic therapies, for patients with various cancers.
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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".