Fatty acid synthase is a metabolic marker of cell proliferation rather than malignancy in ovarian cancer and its precursor cells
Bibliographic record
Abstract
Ovarian cancer (OC) is caused by genetic aberrations in networks that control growth and survival. Importantly, aberrant cancer metabolism interacts with oncogenic signaling providing additional drug targets. Tumors overexpress the lipogenic enzyme fatty acid synthase (FASN) and are inhibited by FASN blockers, whereas normal cells are FASN-negative and FASN-inhibitor-resistant. Here, we demonstrate that this holds true when ovarian/oviductal cells reside in their autochthonous tissues, whereas in culture they express FASN and are FASN-inhibitor-sensitive. Upon subculture, nonmalignant cells cease growth, express senescence-associated β-galactosidase, lose FASN and become FASN-inhibitor-resistant. Immortalized ovarian/oviductal epithelial cell lines—although resisting senescence—reveal distinct growth activities, which correlate with FASN levels and FASN drug sensitivities. Accordingly, ectopic FASN stimulates growth in these cells. Moreover, FASN levels and lipogenic activities affect cellular lipid composition as demonstrated by thin-layer chromatography. Correlation between proliferation and FASN levels was finally evaluated in cancer cells such as HOC-7, which contain subclones with variable differentiation/senescence and corresponding FASN expression/FASN drug sensitivity. Interestingly, senescent phenotypes can be induced in parental HOC-7 by differentiating agents. In OC cells, FASN drugs induce cell cycle blockade in S and/or G2/M and stimulate apoptosis, whereas in normal cells they only cause cell cycle deceleration without apoptosis. Thus, normal cells, although growth-inhibited, may survive and recover from FASN blockade, whereas malignant cells get extinguished. FASN expression and FASN drug sensitivity are directly linked to cell growth and correlate with transformation/differentiation/senescence only indirectly. FASN is therefore a metabolic marker of cell proliferation rather than a marker of malignancy and is a useful target for future drug development.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".