Interaction between cyclooxygenase-2, Snail, and E-cadherin in gastric cancer cells
Bibliographic record
Abstract
AIM: To investigate the mechanisms of how cyclooxygenase-2 (COX-2) regulates E-cadherin in gastric cancer cells. METHODS: COX-2 expression in human gastric cancer cell lines SGC-7901, BGC-823, MGC-803 and AGS were measured at the mRNA and protein level. COX-2 rich cell line SGC-7901 was chosen for subsequent experiments. siRNA mediated gene knockdown was used to investigate the impact of COX-2 on nuclear factor-κB (NF-κB), Snail, and E-cadherin in gastric cancer cells. Gene expression was determined by Western blot and real-time polymerase chain reaction. To analyze whether NF-κB inhibition could interrupt the modulatory effect of COX-2 or prostaglandin E2 (PGE2) on E-cadherin, gastric cancer cells were treated with celecoxib or PGE2, in the presence of NF-κB specific siRNA. RESULTS: Highest expression level of COX-2 was found in SGC-7901 cells, both at mRNA and protein levels. siRNA mediated down-regulation of COX-2 led to a reduced expression of NF-κB and Snail, but an increased expression of E-cadherin in SGC-7901 cells. siRNA mediated down-regulation of NF-κB also led to a reduced expression of E-cadherin and Snail in SGC-7901 cells. However, COX-2 expression did not alter after cells were treated with NF-κB specific siRNA in SGC-7901 cells. Treatment of SGC-7901 cells with celecoxib led to a reduced expression of Snail but an increased expression of E-cadherin. In contrast, treatment of SGC-7901 cells with PGE2 led to an increased Snail and a decreased E-cadherin. However, siRNA-mediated knockdown of NF-κB partially abolished the effect of celecoxib and PGE2 on the regulation of E-cadherin and Snail in SGC-7901 cells. CONCLUSION: COX-2 likely functions upstream of NF-κB and regulates the expression of E-cadherin via NF-κB/Snail signaling pathway in gastric cancer cells.
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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".