The Urokinase-Type Plasminogen Activator Receptor (uPAR) as a Mediator of Physiological and Pathological Processes: Potential Therapeutic Strategies
Bibliographic record
Abstract
The urokinase-type plasminogen activator receptor (uPAR) plays a pivotal role in regulating extracellular proteolysis, cell migration, immune responses, and tissue remodeling across diverse physiological and pathological contexts. This review provides detailed insights into the structure of uPAR, ligand interactions, and signaling mechanisms, emphasizing its central function in cancer progression, including tumor invasion, metastasis, angiogenesis, and modulation of the tumor microenvironment. We also summarize the involvement of uPAR as a key player in cardiovascular, infectious, and neurological diseases, where it contributes to inflammation, tissue damage, and disease progression. However, translational gaps remain, most notably inconsistent assay harmonization (especially for suPAR), uncertain context-specific cut-offs and patient-selection criteria and limited multicenter validation for uPAR-targeted imaging and therapeutics. This review addresses these gaps by synthesizing cross-disease evidence to clarify clinical use cases and outline practical selection frameworks. Furthermore, we discuss the clinical potential of uPAR as a diagnostic and prognostic biomarker in diverse disease contexts, along with recent advances in therapeutic strategies targeting uPAR.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".