Genetic Deletion of <i>mPGES-1</i> Suppresses Intestinal Tumorigenesis
Bibliographic record
Abstract
Elevated levels of prostaglandin E(2) (PGE(2)) are often found in colorectal cancers. Thus, nonsteroidal anti-inflammatory drugs, including selective cyclooxygenase-2 (COX-2) inhibitors, are among the most promising chemopreventive agents for colorectal cancer. However, their long-term use is restricted by the occurrence of adverse events believed to be associated with a global reduction in prostaglandin production. In the present study, we evaluated the chemopreventive efficacy of targeting the terminal synthase microsomal PGE(2) synthase 1 (mPGES-1), which is responsible for generating PGE(2), in two murine models of intestinal cancer. We report for the first time that genetic deletion of mPGES-1 in Apc-mutant mice results in marked and persistent suppression of intestinal cancer growth by 66%, whereas suppression of large adenomas (>3 mm) was almost 95%. This effect occurred despite loss of Apc heterozygosity and beta-catenin activation. However, we found that mPGES-1 deficiency was associated with a disorganized vascular pattern within primary adenomas as determined by CD31 immunostaining. We also examined the effect of mPGES-1 deletion on carcinogen-induced colon cancer. The absence of mPGES-1 reduced the size and number of preneoplastic aberrant crypt foci (ACF). Importantly, mPGES-1 deletion also blocked the nuclear accumulation of beta-catenin in ACF, confirming that beta-catenin is a critical target of PGE(2) procarcinogenic signaling in the colon. Our data show the feasibility of targeting mPGES-1 for cancer chemoprevention with the potential for improved tolerability over traditional nonsteroidal anti-inflammatory drugs and selective COX-2 inhibitors.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 0.001 |
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".