Tobacco Smoke Induces Urokinase-Type Plasminogen Activator and Cell Invasiveness: Evidence for an Epidermal Growth Factor Receptor–Dependent Mechanism
Bibliographic record
Abstract
Multiple tobacco smoke-related premalignant and malignant lesions develop synchronously or metachronously in various organ sites, including the oral cavity. Both field cancerization and clonal migration seem to contribute to the occurrence of multiple tumors. Although the importance of endogenous factors (e.g., oncogenes) in regulating clonal migration is well established, little is known about the role of exogenous factors. Hence, the main objective of this study was to elucidate the mechanism by which tobacco smoke stimulated the migration of cells through extracellular matrix (ECM). Treatment of MSK-Leuk1 cells with a saline extract of tobacco smoke induced the migration of cells through ECM. Tobacco smoke induced the expression of urokinase-type plasminogen activator (uPA), resulting in plasmin-dependent degradation of ECM and increased cell migration. AG1478, a small-molecule inhibitor of the epidermal growth factor receptor (EGFR) tyrosine kinase, a neutralizing antibody to EGFR, or an antibody to amphiregulin, an EGFR ligand, also blocked tobacco smoke-mediated induction of uPA and cell migration through ECM. PD98059, an inhibitor of mitogen-activated protein kinase (MAPK) kinase activity, caused similar inhibitory effects. Taken together, these results suggest that tobacco smoke activated the EGFR-->extracellular signal-regulated kinase 1/2 MAPK pathway, causing induction of uPA. This led, in turn, to increased plasmin-dependent degradation of matrix proteins and enhanced cell migration through ECM. These data strongly suggest that chemicals in tobacco smoke can mimic the effects of oncogenes in regulating uPA-dependent cell invasion through ECM. These findings also strengthen the rationale for determining whether inhibitors of EGFR tyrosine kinase reduce the risk of tobacco smoke-related second primary tumors.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".