Identification of a Prostate Cancer Cell Proteinase Activated Receptor/MMP Signaling Axis
Bibliographic record
Abstract
Hypothesis Proteinase Activated Receptors (PARs) are G‐Protein‐Coupled Receptors activated by the proteolytic unmasking of a cryptic tethered ligand. We proposed that prostate cancer cells produce PAR‐regulating enzymes. Methods We monitored PC3, DU145 and LNCaP cell supernatant cleavage of PARs in intact cells by fluorescence imaging using N‐ and C‐ terminus dual‐fluorophore‐tagged PARs. Loss of the N‐terminal tag denotes receptor cleavage‐activation. We also used N‐terminal nano‐luciferase‐tagged PARs that, via luciferase release, document PAR‐cleaving proteinase activity in cell‐derived supernatants. MAPKinase and transwell migration assays were used to monitor PAR activation in PC3 cells. Results Activation of PARs 1 & 2 triggered MAPKinase signaling and cancer cell migration. Distinct PAR‐cleaving proteinases in conditioned media from all three prostate cancer cell lines were observed with the N‐Luc‐Luciferase assay, with differential inhibition by matrix metalloproteinase inhibitors and soya trypsin inhibitor. Further, when expressed in PC3 cells, the dual‐fluorophore‐tagged PAR1 was seen in a constitutively cleaved‐active state, with an absent N‐terminus. Conclusion Our data show that prostate cancer‐derived cells secrete PAR‐regulating proteinases that, via an autocrine process, can regulate PAR function in the tumour microenvironment. Since PAR activation triggers prostate cell migration and MAPKinase, PAR antagonists and inhibitors of PAR‐activating proteinases represent therapeutic targets for treating prostate cancer. Funding Prostate Cancer Canada Movember Discovery grant.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".