Further Understanding of Urokinase Plasminogen Activator Overexpression in Urothelial Bladder Cancer Progression, Clinical Outcomes and Potential Therapeutic Targets
Bibliographic record
Abstract
PURPOSE: The Plasminogen Activation System (PAS) plays a role in tumor growth, invasion and metastasis and has been associated with oncological outcomes in urinary bladder carcinoma (UBC). The use of the different components of this system as molecular markers could improve our understanding of the heterogeneous behavior of UBC and might enable earlier disease detection, individual risk stratification, more accurate outcome prediction and be a rationale for new targeted therapies. METHODS: A comprehensive literature search including relevant articles up to October 2020 was performed using the MEDLINE/PubMed database. RESULTS: The components of the PAS axis are involved in tumor progression through their signaling processes during angiogenesis, cell migration, metastasis and adhesion. The body of evidence shows an association of PAS component overexpression with adverse pathological features and clinical outcome in UBC. Overexpressed PAS components correlate with a higher pathological tumor grade and advanced tumor stage. In non-muscle-invasive bladder cancer (NMIBC), the PAS components were associated with disease outcome while in muscle-invasive bladder cancer (MIBC), it was associated with disease outcome and pathological features. Possible therapeutic approaches in the PAS for the treatment of UBC have only been sparsely investigated in in vitro and in vivo studies. Intravesical plasminogen activator inhibitor 1 (PAI-1) instillation in animal models yielded interesting results and warrant further exploration in Phase II studies. CONCLUSION: The overexpression of PAS components in UBC tumor tissue is associated with adverse pathological features and worse oncological outcomes. These findings are mainly based on preclinical studies and retrospective series, which requires further prospective studies to translate the PAS into clinically useful biomarkers and therapeutic targets.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".