Oncogenic Fatty Acid Metabolism Rewires Energy Supply Chain in Gastric Carcinogenesis
Bibliographic record
Abstract
BACKGROUND & AIMS: Gastric carcinogenesis develops within a sequential carcinogenic cascade from precancerous metaplasia to dysplasia and adenocarcinoma, and oncogenic gene activation can drive the process. Metabolic reprogramming is considered a key mechanism for cancer cell growth and proliferation. However, how metabolic changes contribute to the progression of metaplasia to dysplasia remains unclear. We have examined metabolic dynamics during gastric carcinogenesis using a novel mouse model that induces Kras activation in zymogen-secreting chief cells. METHODS: (GCK) mouse model that continuously induces active Kras expression in chief cells after doxycycline treatment. Histologic examination and imaging mass spectrometry were performed in the GCK mouse stomachs at 2 to 14 weeks after doxycycline treatment. Mouse and human gastric organoids were used for metabolic enzyme inhibitor treatment. The GCK mice were treated with a stearoyl- coenzyme A desaturase (SCD) inhibitor to inhibit the fatty acid desaturation. Tissue microarrays were used to assess the SCD expression in human gastrointestinal cancers. RESULTS: The GCK mice developed metaplasia and high-grade dysplasia within 4 months. Metabolic reprogramming from glycolysis to fatty acid metabolism occurred during metaplasia progression to dysplasia. Altered fatty acid desaturation through SCD produces a novel eicosenoic acid, which fuels dysplastic cell hyperproliferation and survival. The SCD inhibitor killed both mouse and human dysplastic organoids and selectively targeted dysplastic cells in vivo. SCD was up-regulated during carcinogenesis in human gastrointestinal cancers. CONCLUSIONS: Active Kras expression only in gastric chief cells drives the full spectrum of gastric carcinogenesis. Also, oncogenic metabolic rewiring is an essential adaptation for high-energy demand in dysplastic cells.
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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".