Metastatic phenotype and immunosuppressive tumour microenvironment in pancreatic ductal adenocarcinoma: Key role of the urokinase plasminogen activator (PLAU)
Bibliographic record
Abstract
Background Previous studies have revealed the role of dysregulated urokinase plasminogen activator (encoded by PLAU ) expression and activity in several pathways associated with cancer progression. However, systematic investigation into the association of PLAU expression with factors that modulate PDAC (pancreatic ductal adenocarcinoma) progression is lacking, such as those affecting stromal (pancreatic stellate cell, PSC)-cancer cell interactions, tumour immunity, PDAC subtypes and clinical outcomes from potential PLAU inhibition. Methods This study used an integrated bioinformatics approach to identify prognostic markers correlated with PLAU expression using different transcriptomics, proteomics, and clinical data sets. We then determined the association of dysregulated PLAU and correlated signatures with oncogenic pathways, metastatic phenotypes, stroma, immunosuppressive tumour microenvironment (TME) and clinical outcome. Finally, using an in vivo orthotopic model of pancreatic cancer, we confirmed the predicted effect of inhibiting PLAU on tumour growth and metastasis. Results Our analyses revealed that PLAU upregulation is not only associated with numerous other prognostic markers but also associated with the activation of various oncogenic signalling pathways, aggressive phenotypes relevant to PDAC growth and metastasis, such as proliferation, epithelial-mesenchymal transition (EMT), stemness, hypoxia, extracellular cell matrix (ECM) degradation, upregulation of stromal signatures, and immune suppression in the tumour microenvironment (TME). Moreover, the upregulation of PLAU was directly connected with signalling pathways known to mediate PSC-cancer cell interactions. Furthermore, PLAU upregulation was associated with the aggressive basal/squamous phenotype of PDAC and significantly reduced overall survival, indicating that this subset of patients may benefit from therapeutic interventions to inhibit PLAU activity. Our studies with a clinically relevant orthotopic pancreatic model showed that even short-term PLAU inhibition is sufficient to significantly halt tumour growth and, importantly, eliminate visible metastasis. Conclusion Elevated PLAU correlates with increased aggressive phenotypes, stromal score, and immune suppression in PDAC. PLAU upregulation is also closely associated with the basal subtype type of PDAC; patients with this subtype are at high risk of mortality from the disease and may benefit from therapeutic targeting of PLAU .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".