Matrix Metalloproteinase-19 Promotes Metastatic Behavior <i>In Vitro</i> and is Associated with Increased Mortality in Non–Small Cell Lung Cancer
Bibliographic record
Abstract
RATIONALE: Lung cancer is the leading cause of cancer death in both men and women in the United States and worldwide. Matrix metalloproteinases (MMPs) have been implicated in the development and progression of lung cancer, but their role in the molecular pathogenesis of lung cancer remains unclear. We have found that MMP19, a relatively novel member of the MMP family, is overexpressed in lung tumors when compared with control subjects. OBJECTIVES: To test the hypothesis that MMP19 plays a significant role in the development and progression of non-small cell lung cancer (NSCLC). METHODS: We have analyzed lung cancer gene expression data, immunostained lung tumors for MMP19, and performed in vitro assays to test the effects of MMP19 in NSCLC cells. MEASUREMENTS AND MAIN RESULTS: We found that MMP19 gene and protein expression is increased in lung cancer tumors compared with adjacent and histologically normal lung tissues. In three independent datasets, increased MMP19 gene expression conferred a poorer prognosis in NSCLC. In vitro, we found that overexpression of MMP19 promotes epithelial-mesenchymal transition, migration, and invasiveness in multiple NSCLC cell lines. Overexpression of MMP19 with a mutation at the catalytic site did not impair epithelial-mesenchymal transition or expression of prometastasis genes. We also found that miR-30 isoforms, a microRNA family predicted to target MMP19, is markedly down-regulated in human lung cancer and regulates MMP19 expression. CONCLUSIONS: Taken together, these findings suggest that MMP19 is associated with the development and progression of NSCLC and may be a potential biomarker of disease severity and outcome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".