Inhibition of Epidermal Growth Factor Receptor Signaling Elevates 15-Hydroxyprostaglandin Dehydrogenase in Non–Small-Cell Lung Cancer
Bibliographic record
Abstract
Evidence indicates that the induction of cyclooxygenase-2 (COX-2) and high prostaglandin E2 (PGE2) levels contribute to the pathogenesis of non-small-cell lung cancer (NSCLC). In addition to overproduction by COX-2, PGE2 concentrations also depend upon the levels of the PGE2 catabolic enzyme 15-hydroxyprostaglandin dehydrogenase (15-PGDH). We find a dramatic down-regulation of PGDH protein in NSCLC cell lines and in resected human tumors when compared with matched normal lung. Affymetrix array analysis of 10 normal lung tissue samples and 49 resected lung tumors revealed a much lower expression of PGDH transcripts in all NSCLC histologic groups. In addition, treatment with the epidermal growth factor receptor tyrosine kinase inhibitor (EGFR TKI) erlotinib increased the expression of 15-PGDH in a subset of NSCLC cell lines. This effect may be due in part to an inhibition of the extracellular signal-regulated kinase (ERK) pathway as treatment with mitogen-activated protein kinase kinase (MEK) inhibitor U0126 mimics the erlotinib results. We show by quantitative reverse transcription-PCR that the transcript levels of ZEB1 and Slug transcriptional repressors are dramatically reduced in a responsive cell line upon EGFR and MEK/ERK inhibition. In addition, the Slug protein, but not ZEB1, binds to the PGDH promoter and represses transcription. As these repressors function by recruiting histone deacetylases to promoters, it is likely that PGDH is repressed by an epigenetic mechanism involving histone deacetylation, resulting in increased PGE2 activity in tumors. This effect is reversible in a subset of NSCLC upon treatment with an EGFR TKI.
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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) | 0.001 | 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".