Cyclooxygenase-2–Dependent Regulation of E-Cadherin: Prostaglandin E2 Induces Transcriptional Repressors ZEB1 and Snail in Non–Small Cell Lung Cancer
Bibliographic record
Abstract
Elevated tumor cyclooxygenase-2 (COX-2) expression is associated with tumor invasion, metastasis, and poor prognosis in non-small cell lung cancer (NSCLC). Here, we report that COX-2-dependent pathways contribute to the modulation of E-cadherin expression in NSCLC. First, whereas genetically modified COX-2-sense (COX-2-S) NSCLC cells expressed low E-cadherin and showed diminished capacity for cellular aggregation, genetic or pharmacologic inhibition of tumor COX-2 led to increased E-cadherin expression and resulted in augmented homotypic cellular aggregation among NSCLC cells in vitro. An inverse relationship between COX-2 and E-cadherin was shown in situ by double immunohistochemical staining of human lung adenocarcinoma tissue sections. Second, treatment of NSCLC cells with exogenous prostaglandin E(2) (PGE(2)) significantly decreased the expression of E-cadherin, whereas treatment of COX-2-S cells with celecoxib (1 mumol/L) led to increased E-cadherin expression. Third, the transcriptional suppressors of E-cadherin, ZEB1 and Snail, were up-regulated in COX-2-S cells or PGE(2)-treated NSCLC cells but decreased in COX-2-antisense cells. PGE(2) exposure led to enhanced ZEB1 and Snail binding at the chromatin level as determined by chromatin immunoprecipitation assays. Small interfering RNA-mediated knockdown of ZEB1 or Snail interrupted the capacity of PGE(2) to down-regulate E-cadherin. Fourth, an inverse relationship between E-cadherin and ZEB1 and a direct relationship between COX-2 and ZEB1 were shown by immunohistochemical staining of human lung adenocarcinoma tissue sections. These findings indicate that PGE(2), in autocrine or paracrine fashion, modulates transcriptional repressors of E-cadherin and thereby regulates COX-2-dependent E-cadherin expression in NSCLC. Thus, blocking PGE(2) production or activity may contribute to both prevention and treatment of NSCLC.
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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 | 0.000 |
| 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) | 0.002 | 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".