The potential role of AhR/NR4A1 in androgen-dependent prostate cancer: focus on TCDD-induced ferroptosis
Bibliographic record
Abstract
Prostate cancer (PCa) is a complex disease with diverse molecular alterations. The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor that exhibits pleiotropic roles in PCa, and 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is a potent ligand for AhR. While targeting ferroptosis is an innovative PCa therapeutic strategy, the impact of AhR on this process remains unclear. This study aimed to investigate the influence of AhR on lipid peroxidation and ferroptosis. Results showed that TCDD activated AhR, as evidenced by increased CYP1A1 expression, leading to reduced cell viability. TCDD caused mitochondria shrinkage, decreased the GSH/GSSG ratio, and elevated the MDA levels and lipid peroxidation. Interestingly, AhR knockdown reversed these effects, similar to the action of ferroptosis inhibitors. Mechanistically, TCDD suppressed nuclear receptor subfamily 4 group A member 1 (NR4A1) expression, in part due to AhR activation. This suppression subsequently led to a reduction in the expression of the NR4A1 downstream target stearoyl-CoA desaturase 1 (SCD1). NR4A1 overexpression counteracted the effects of TCDD. In vivo, TCDD activated AhR, downregulated NR4A1 and SCD1 expression, induced mitochondria shrinkage, and increased the MDA and 4-hydroxynonenal (4-HNE) levels. In summary, TCDD promotes ferroptosis in androgen-dependent PCa via inhibiting the NR4A1/SCD1 axis, in part dependent on AhR activation.
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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 | 0.001 |
| 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".