Proteolytic Signal Crosstalk in the Prostate Cancer Microenvironment: PC3 Cell Metalloproteinases and Autocrine‐paracrine‐fibroblast Regulation of Proteinase‐activated Receptors (PARs)
Bibliographic record
Abstract
Tumour‐associated fibroblasts(TAFs) and non‐tumour epithelial cells (NT‐EPCs) are known to respond to tumour‐derived microenvironment agonists. We hypothesized that prostate cancer‐derived PC3 cells can signal to TAFs and NT‐EPCs by generating proteinases that can regulate proteinase‐activated receptors(PARs). We thus expressed Dual‐Tagged N‐terminal‐mCherry/C‐terminal‐YFP PAR1 in PC3 cells and in a non‐tumour prostate epithelial cell line, RWPE. N‐terminal N‐luciferase(LUC)‐tagged PARs 1,2 and 4 were expressed in WI38 fibroblasts. Upon live cell imaging, intact expressed dual‐tagged PARs appear ‘yellow’; cleaved dual‐tagged‐PARs appear green (loss of N‐terminal mCherry). Cleavage of N‐LUC‐PARs expressed in WI38 cells releases a N‐LUC fluorescence signal into the supernatant. Imaging showed that PC3 cell‐expressed dual‐tagged PAR1 appeared ‘green’, demonstrating autocrine PAR cleavage. Addition of a general MMP inhibitor(GM6001) blocked PC3‐expressed dual‐tagged‐PAR1 cleavage (yellow receptor) as did CRISPR‐elimination of PC3‐expressed MMP2. PC3‐derived supernatants cleaved tagged PAR1 expressed in RWPE cells (green). PC3‐derived supernatants also cleaved all of WI38 cell expressed N‐LUC‐PARs 1, 2 & 4, releasing fluorescence from the cells. We conclude that prostate cancer‐derived PC3 cells produce PAR‐regulating proteinases, including MMP2, that can regulate tumour and non‐tumour cell PAR signalling by an autocrine and paracrine mechanism in a tumour microenvironment.
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) | 0.002 | 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".