Proteinase-activated Receptor-2 Induces Cyclooxygenase-2 Expression through β-Catenin and Cyclic AMP-response Element-binding Protein
Bibliographic record
Abstract
Chronic inflammation of mucosae is associated with an increased cancer risk. Tumorigenesis in these tissues is associated with the activity of some proteinases, cyclooxygenase-2 (COX-2), and beta-catenin. Serine proteinases participate in both inflammation and tumorigenesis through the activation of proteinase-activated receptor-2 (PAR(2)), which up-regulates COX-2 by an unknown mechanism. We sought to determine whether beta-catenin participated in PAR(2)-induced COX-2 expression and through what cellular mechanism. In A549 epithelial cells, we showed that PAR(2) activation increased COX-2 expression through the beta-catenin/T cell factor transcription pathway. This effect was dependent upon ERK1/2 MAPK, which inhibited the beta-catenin-regulating protein, glycogen synthase kinase-3beta, and induced the activity of the cAMP-response element-binding protein (CREB). Knockdown of CREB by small interfering RNA revealed that PAR(2)-induced beta-catenin transcriptional activity and COX-2 expression were CREB-dependent. A co-immunoprecipitation assay revealed a physical interaction between CREB and beta-catenin. Thus, PAR(2) up-regulated COX-2 expression via an ERK1/2-mediated activation of the beta-catenin/Tcf-4 and CREB pathways. These findings reveal new cellular mechanisms by which serine proteinases may participate in tumor development and are particularly relevant to cancers associated with chronic mucosal inflammation, where serine proteinases are abundant and COX-2 overexpression is a common feature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".