Carnitine palmitoyltransferase 1C promotes cell survival and tumor growth under conditions of metabolic stress
Bibliographic record
Abstract
Tumor cells gain a survival/growth advantage by adapting their metabolism to respond to environmental stress, a process known as metabolic transformation. The best-known aspect of metabolic transformation is the Warburg effect, whereby cancer cells up-regulate glycolysis under aerobic conditions. However, other mechanisms mediating metabolic transformation remain undefined. Here we report that carnitine palmitoyltransferase 1C (CPT1C), a brain-specific metabolic enzyme, may participate in metabolic transformation. CPT1C expression correlates inversely with mammalian target of rapamycin (mTOR) pathway activation, contributes to rapamycin resistance in murine primary tumors, and is frequently up-regulated in human lung tumors. Tumor cells constitutively expressing CPT1C show increased fatty acid (FA) oxidation, ATP production, and resistance to glucose deprivation or hypoxia. Conversely, cancer cells lacking CPT1C produce less ATP and are more sensitive to metabolic stress. CPT1C depletion via siRNA suppresses xenograft tumor growth and metformin responsiveness in vivo. CPT1C can be induced by hypoxia or glucose deprivation and is regulated by AMPKα. Cpt1c-deficient murine embryonic stem (ES) cells show sensitivity to hypoxia and glucose deprivation and altered FA homeostasis. Our results indicate that cells can use a novel mechanism involving CPT1C and FA metabolism to protect against metabolic stress. CPT1C may thus be a new therapeutic target for the treatment of hypoxic tumors.
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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".