Inhibition of lipid metabolism exerts antitumor effects on rhabdomyosarcoma
Bibliographic record
Abstract
Abstract Background and aims: Rhabdomyosarcoma (RMS) exhibits tumor-specific energy metabolic changes that include the Warburg effect. Since targeting cancer metabolism is a promising therapeutic approach, we examined the antitumor effects of suppressing lipid metabolism in RMS.Methods: We suppressed lipid metabolism in RMS cells in vitro by administering an inhibitor of malonyl-CoA decarboxylase, which increases malonyl CoA and decreases fatty acid oxidation. In vivo studies involved injection of human Rh30 cells into the gastrocnemius muscle of 6-week-old female nude mice, which were divided into two groups: normal chow diet group (NCD) and low-fat diet group (LFD).Results: Suppression of lipid metabolism in RMS cells decreased cell proliferation by inducing cell cycle arrest. Metabolomic analysis showed an increase in glycolysis and inactivation of the pentose phosphate pathway. Immunoblotting analysis revealed upregulated expression of LC3A/B-II, an autophagy marker, due to increased phosphorylation of AMP-activated protein kinase (AMPK), a nutrient sensor. Inhibition of both lipid metabolism and autophagy suppressed tumor proliferation and increased apoptosis. Moreover, mice fed a LFD for 21-days showed reduced tumor growth compared to NCD-fed mice. Conclusion: Suppression of lipid metabolism disrupted the equilibrium of the cancer-specific metabolism in RMS, resulting in a tumor growth-inhibitory effect. Therefore, the development of treatments focusing on the lipid dependence of RMS is highly promising.
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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".