Glucagon-Like Peptide-1 Receptor Activation Inhibits Growth and Augments Apoptosis in Murine CT26 Colon Cancer Cells
Bibliographic record
Abstract
Obesity, accompanying or independent of type 2 diabetes mellitus (T2DM), is associated with higher rates of malignancy. Hence, there is considerable interest in understanding whether therapies used to treat obese patients with T2DM impact cancer cell growth. Glucagon-like peptide-1 (GLP-1) is produced in enteroendocrine cells and secreted after meal ingestion. GLP-1 regulates blood glucose through multiple mechanisms, principally inhibition of glucagon and stimulation of insulin secretion. GLP-1 also exerts independent effects promoting cell growth and survival, and sustained activation of GLP-1 receptor (GLP-1R) signaling in rodent thyroid glands leads to C-cell hyperplasia and medullary thyroid cancer. Hence, whether therapies based on GLP-1R activation modify growth or survival of cancer cells is of ongoing interest. We studied the biological actions of GLP-1 in mouse CT26 colon cancer cells that express a functional GLP-1R. The GLP-1R agonist exendin (Ex)-4 (exenatide) increased intracellular cAMP levels and inhibited the activity of signaling kinases glycogen synthase kinase 3 and ERK1/2 in CT26 cells. The Ex-4-induced inactivation of glycogen synthase kinase 3, but not ERK1/2, was dependent on protein kinase A and blocked by the GLP-1R antagonist Ex(9-39). Furthermore, Ex-4 altered cell morphology, induced apoptosis, and inhibited proliferation of CT26 cells in vitro. Moreover Ex-4 decreased CT26 colony formation in soft agar and augmented apoptosis induced by irinotecan. Twice-daily treatment of CT26 tumor-bearing BALB/c mice with Ex-4 for 2 wk increased tumor apoptosis. Hence, GLP-1R activation reduces growth and survival in CT26 colon cancer cells that express the endogenous classical GLP-1R.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".