Glucagon-like Peptide-2 Does Not Modify the Growth or Survival of Murine or Human Intestinal Tumor Cells
Bibliographic record
Abstract
Glucagon-like peptide-2 (GLP-2) secreted from enteroendocrine cells exerts proabsorptive, regenerative, and cytoprotective actions in the normal and injured gut epithelium. Hence, sustained GLP-2 receptor (GLP-2R) activation represents a strategy under investigation for the prevention and treatment of chemotherapy-induced mucositis. Nevertheless, the consequences of increased GLP-2R signaling for the growth and survival of intestinal tumor cells remain poorly understood. We studied the proliferative and cytoprotective actions of GLP-2 in human colon cancer cells stably transfected with the GLP-2R and in nude mice harboring GLP-2R(+) human colon cancer cells. The importance of the GLP-2R for tumor growth was also examined in Apc(Min/+) mice chronically treated with exogenous GLP-2 and in Apc(Min/+):Glp2r(-/-) mice. GLP-2 increased cyclic AMP accumulation and produced cell-specific activation of growth and survival pathways in DLD-1, SW480, and HT29 cells. However, GLP-2 did not stimulate cell growth or attenuate cycloheximide-, LY294002-, indomethacin-, or chemotherapy-induced cytotoxicity in vitro. Moreover, chronic GLP-2 administration had no effect on the growth of human colon cancer cell xenografts in nude mice in vivo. Daily GLP-2 treatment for 7 weeks increased growth of normal gut mucosa but did not increase the number or size of polyps in Apc(Min/+) mice, and genetic disruption of the Glp2r gene in Apc(Min/+) mice did not modify polyp size or number. Taken together, although GLP-2R activation engages signaling pathways promoting cell proliferation and cytoprotection in the normal gut epithelium, sustained direct or indirect modulation of GLP-2R signaling does not modify intestinal tumor cell growth or survival.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".