Autocrine proteinase-activated receptor signaling in PC3 prostate cancer cells
Bibliographic record
Abstract
Proteinase-activated receptors (PARs) are G protein-coupled receptors (GPCRs) activated by limited n-terminal proteolysis. PARs are highly expressed in many cancer cells, including prostate cancer (PCa), and regulate various aspects of tumor growth and metastasis. Specific activators of PARs in different physiological and pathophysiological contexts remain poorly defined. In this study, we examined the androgen-independent human prostatic cancer cell line PC3 and find the functional expression of PAR1 and PAR2, but not PAR4. Using genetically encoded PAR cleavage biosensors, we showed that PC3 cells secrete proteolytic enzymes that cleave PARs and trigger autocrine signaling. CRISPR/Cas9 targeting of PAR1 and PAR2 combined with microarray analysis revealed genes that are regulated through this autocrine signaling mechanism. We found several genes that are known PCa prognostic factors or biomarker to be differentially expressed in the PAR1-knockout (KO) and PAR2-KO PC3 cells. We further examined PAR1 and PAR2 regulation of PCa cell proliferation and migration and found that absence of PAR1 promotes PC3 cell migration and suppresses cell proliferation, whereas PAR2 deficiency showed opposite effects. Overall, these results demonstrate that autocrine signaling through PARs is an important regulator of PCa cell function.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".