Inhibition of invasion, angiogenesis, tumor growth, and metastasis by adenovirus-mediated transfer of antisense uPAR and MMP-9 in non–small cell lung cancer cells
Why this work is in the frame
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Bibliographic record
Abstract
Lung cancer is currently the leading cause of cancer deaths in the United States. Conventional therapeutic treatments, including surgery, chemotherapy, and radiation therapy, have achieved only limited success. The overexpression of proteases, such as urokinase-type plasminogen activator (uPA), its receptor (uPAR), and matrix metalloproteinases (MMP), is correlated with the progression of lung cancer. In the present study, we used a replication-deficient adenovirus capable of expressing antisense uPAR and antisense MMP-9 transcripts to simultaneously down-regulate uPAR and MMP-9 in H1299 cells. Ad-uPAR-MMP-9 infection of H1299 cells resulted in a dose- and time-dependent decrease of uPAR protein levels and MMP-9 activity as determined by Western blotting and gelatin zymography, respectively. Corresponding immunohistochemical analysis also showed that Ad-uPAR-MMP-9 infection inhibited uPAR and MMP-9 expression. As shown by Boyden chamber assay, Ad-uPAR-MMP-9 infection significantly decreased the invasive capacity of H1299 cells compared with mock and Ad-CMV (empty vector)-infected cells in vitro. Furthermore, Ad-uPAR-MMP-9 infection inhibited capillary-like structure formation in H1299 cells cocultured with endothelial cells in a dose-dependent manner compared with mock- and Ad-CMV-infected cells. Ad-uPAR-MMP-9 injection caused the regression of s.c. induced tumors after s.c. injection with H1299 lung cancer cells and inhibited lung metastasis in the metastatic model with A549 cells. These data suggest that Ad-uPAR-MMP-9 shows its antitumor activity against both established and early phases of lung cancer metastases by causing the destruction of the tumor vasculature. In summary, adenovirus-mediated inhibition of uPA-uPAR interaction and MMP-9 on the cell surface may be a promising anti-invasion and antimetastatic strategy for cancer gene therapy.
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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) | 0.000 | 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) | 0.000 | 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 it