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
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".