Alcohol Metabolism Enriches Squamous Cell Carcinoma Cancer Stem Cells That Survive Oxidative Stress via Autophagy
Bibliographic record
Abstract
BACKGROUND: Alcohol (ethanol) consumption is a major risk factor for head and neck and esophageal squamous cell carcinomas (SCCs). However, how ethanol (EtOH) affects SCC homeostasis is incompletely understood. METHODS: We utilized three-dimensional (3D) organoids and xenograft tumor transplantation models to investigate how EtOH exposure influences intratumoral SCC cell populations including putative cancer stem cells defined by high CD44 expression (CD44H cells). RESULTS: Using 3D organoids generated from SCC cell lines, patient-derived xenograft tumors, and patient biopsies, we found that EtOH is metabolized via alcohol dehydrogenases to induce oxidative stress associated with mitochondrial superoxide generation and mitochondrial depolarization, resulting in apoptosis of the majority of SCC cells within organoids. However, CD44H cells underwent autophagy to negate EtOH-induced mitochondrial dysfunction and apoptosis and were subsequently enriched in organoids and xenograft tumors when exposed to EtOH. Importantly, inhibition of autophagy increased EtOH-mediated apoptosis and reduced CD44H cell enrichment, xenograft tumor growth, and organoid formation rate. CONCLUSIONS: This study provides mechanistic insights into how EtOH may influence SCC cells and establishes autophagy as a potential therapeutic target for the treatment of EtOH-associated SCC.
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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".