Autocrine Proteinase Activated Receptor (PAR) mediated signaling in prostate cancer cells
Bibliographic record
Abstract
ABSTRACT Proteinase activated receptors (PARs) are G protein-coupled receptors (GPCRs) activated by limited N-terminal proteolysis. A variety of proteolytic enzymes derived from the coagulation cascade and inflammatory milieu activate PARs, however specific activators in different physiological and pathophysiological contexts remain poorly defined. PARs are highly expressed in many cancer cells and regulate various aspects of tumor growth and metastasis. Endogenous proteinases that regulate PARs in the setting of various tumors however remains unresolved. Prostate cancer (PCa) remains a major cause of mortality in men despite advances in early detection and clinical intervention. PAR expression has been reported in PCa, however, their role here remains poorly defined. In androgen independent PC3 cells, we find functional expression of PAR1 and PAR2 but not PAR4. Using genetically encoded PAR cleavage biosensors, we find that PCa cells secrete proteolytic enzymes that cleave PARs and trigger autocrine signaling. Deletion of PAR1 and PAR2 using CRISPR/Cas9 combined with microarray analysis revealed genes that are differentially regulated by this autocrine signalling mechanism. Interestingly, several genes that are known PCa prognostic factors or biomarker were differentially expressed in PAR1-KO and PAR2-KO PC3 cells. We also examined PAR1 and PAR2 regulation of PCa cell proliferation and migration using PAR1 and PAR2-KO PC3 cells, as well as PAR1 and PAR2 specific agonists and antagonists. We find that PAR1 and PAR2 have opposite effects on PC3 cell proliferation and migration. In summary, we have identified an autocrine signaling mechanism through PARs as a regulator of PCa cell function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".