Neural signaling modulates metabolism of gastric cancer
Bibliographic record
Abstract
Tumors comprise cancer cells and the associated stromal and immune/inflammatory cells, i.e., tumor microenvironment (TME). Here, we identify a metabolic signature of human and mouse model of gastric cancer and show that vagotomy in the mouse model reverses the metabolic reprogramming, reflected by metabolic switch from glutaminolysis to OXPHOS/glycolysis and normalization of the energy metabolism in cancer cells and TME. We next identify and validate SNAP25, mTOR, PDP1/α-KGDH, and glutaminolysis as drug targets and accordingly propose a therapeutic strategy to target the nerve-cancer metabolism. We demonstrate the efficacy of nerve-cancer metabolism therapy by intratumoral injection of BoNT-A (SNAP25 inhibitor) with systemic administration of RAD001 and CPI-613 but not cytotoxic drugs on overall survival in mice and show the feasibility in patients. These findings point to the importance of neural signaling in modulating the tumor metabolism and provide a rational basis for clinical translation of the potential strategy for gastric cancer.
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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".