Ethanol exposure induces the cancer-associated fibroblast phenotype and lethal tumor metabolism
Bibliographic record
Abstract
Little is known about how alcohol consumption promotes the onset of human breast cancer(s). One hypothesis is that ethanol induces metabolic changes in the tumor microenvironment, which then enhances epithelial tumor growth. To experimentally test this hypothesis, we used a co-culture system consisting of human breast cancer cells (MCF7) and hTERT-immortalized fibroblasts. Here, we show that ethanol treatment (100 mM) promotes ROS production and oxidative stress in cancer-associated fibroblasts, which is sufficient to induce myofibroblastic differentiation. Oxidative stress in stromal fibroblasts also results in the onset of autophagy/mitophagy, driving the induction of ketone body production in the tumor microenvironment. Interestingly, ethanol has just the opposite effect in epithelial cancer cells, where it confers autophagy resistance, elevates mitochondrial biogenesis and induces key enzymes associated with ketone re-utilization (ACAT1/OXCT1). During co-culture, ethanol treatment also converts MCF7 cells from an ER(+) to an ER(-) status, which is thought to be associated with "stemness," more aggressive behavior and a worse prognosis. Thus, ethanol treatment induces ketone production in cancer-associated fibroblasts and ketone re-utilization in epithelial cancer cells, fueling tumor cell growth via oxidative mitochondrial metabolism (OXPHOS). This "two-compartment" metabolic model is consistent with previous historical observations that ethanol is first converted to acetaldehyde (which induces oxidative stress) and then ultimately to acetyl-CoA (a high-energy mitochondrial fuel), or can be used to synthesize ketone bodies. As such, our results provide a novel mechanism by which alcohol consumption could metabolically convert "low-risk" breast cancer patients to "high-risk" status, explaining tumor recurrence or disease progression. Hence, our findings have clear implications for both breast cancer prevention and therapy. Remarkably, our results also show that antioxidants [such as N-acetyl cysteine (NAC)] can effectively reverse or prevent ethanol-induced oxidative stress in cancer-associated fibroblasts, suggesting a novel strategy for cancer prevention. We also show that caveolin-1 and MCT4 protein expression can be effectively used as new biomarkers to monitor oxidative stress induced by ethanol.
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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".