Adenovirus-mediated small interfering RNA against matrix metalloproteinase-2 suppresses tumor growth and lung metastasis in mice
Bibliographic record
Abstract
Matrix metalloproteinases (MMP) are a group of proteinases that have normal physiologic roles degrading and remodeling the extracellular matrix. They also have multiple roles in different stages of tumor progression. Elevated levels of MMPs have been observed in many tumors; these increases have a strong association with the invasive phenotype. MMP-2 and MMP-9 are particularly involved in cancer invasion and metastasis. MMP inhibitors are currently being tested as therapeutic agents for a number of cancers in both preclinical models and in clinical trials. To date, clinical trials using this strategy have had limited efficacy. A major concern is the lack of specificity of commercially available MMP inhibitors. An adenoviral vector expressing small interfering RNA against the MMP-2 gene (Ad-MMP-2) was constructed to specifically inhibit MMP-2 expression. The effect of Ad-MMP-2 on invasion, angiogenesis, tumor growth, and metastasis of A549 lung cancer cell was evaluated. Ad-MMP-2 infection of lung cancer cells showed specific down-regulation of MMP-2 protein, activity, and transcription as determined by Western blotting, gelatin zymography, and reverse transcription-PCR. Ad-MMP-2 inhibition also mitigated lung cancer invasion and migration, and reduced tumor cell-induced angiogenesis in vitro. In an experimental metastatic lung tumor model, treatment of established tumors by Ad-MMP-2 inhibited s.c. tumor growth and formation of lung nodules in mice. Adenoviral-mediated RNA interference against MMP-2 has significant therapeutic potential for lung cancer and exerts some of this effect by inhibiting angiogenesis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".