Urokinase Plasminogen Activator and Urokinase Plasminogen Activator Receptor Mediate Human Stem Cell Tropism to Malignant Solid Tumors
Bibliographic record
Abstract
Human neural and mesenchymal stem cells have been identified for cell-based therapies in regenerative medicine and as vehicles for delivering therapeutic agents to areas of injury and tumors. However, the signals required for homing and recruitment of stem cells to these sites are not well understood. Urokinase plasminogen activator (uPA) and urokinase plasminogen activator receptor (uPAR) are involved in chemotaxis and cell guidance during normal development and are upregulated in invasive tumors. Here we provided evidence that activation of uPA and uPAR in malignant solid tumors (brain, lung, prostate, and breast) augments neural and mesenchymal stem cell tropism. Expression levels of uPAR on human solid tumor cell lines correlated with levels of uPA and soluble uPAR in tumor cell-conditioned media. Cytokine expression profiles of these tumor-conditioned media were determined by protein arrays. Among 79 cytokines investigated, interleukin (IL)-6, IL-8, and monocyte chemoattractant protein-1 were the most highly expressed cytokines in uPAR-positive tumors. We provided evidence that human recombinant uPA induced stem cell migration, whereas depletion of uPA from PC-3 prostate cancer cell-conditioned medium blocked stem cell migration. Furthermore, retrovirus-mediated overexpression of uPA and uPAR in neuroblastoma (NB1691) cells induced robust migration of stem cells toward NB1691 cell-conditioned media, compared with media derived from wild-type NB1691 cells. We conclude that expression of uPA and uPAR in cancer cells underlies a novel mechanism of stem cell tropism to malignant solid tumors, which may be important for development of optimal stem cell-based therapies. Disclosure of potential conflicts of interest is found at the end of this article.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".