mTOR signaling regulates gastric epithelial progenitor homeostasis and gastric tumorigenesis via MEK1-ERKs and BMP-Smad1 pathways
Bibliographic record
Abstract
mTOR, the sensor of nutrients and growth factors, has important roles in tissue homeostasis and tumorigenesis. However, how mTOR controls gastric epithelial cell turnover and gastric cancer development, a leading malignancy, remains poorly understood. Here, we provide genetic evidence that mTOR activation promotes proliferation and inhibits differentiation of Lgr5 + gastric epithelial progenitors (GEPs) in gastric homeostasis and tumorigenesis. mTOR signaling increases MEK1 and Smad1 expression and enhances activation of MEK1-ERKs and BMP-Smad1 pathways, respectively, in GEPs and gastric tumors. Mek1 deletion or inhibition rescues hyperproliferation, whereas Bmpr1a ablation or inhibition rescues differentiation defects of Tsc1 −/− GEPs. Tsc1 deficiency in Lgr5 + GEPs accelerates gastric tumor initiation and development, which require MEK1-ERKs for hyperplasia and BMP-Smad1 for differentiation suppression. These findings reveal how mTOR signaling controls Lgr5 + GEP homeostasis and cancerization and suggest that ERKs and Smad1 signaling can be safely targeted to substitute mTOR inhibitors in gastric cancer therapy.
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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) | 0.001 | 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".