Thyroid hormone receptor beta signaling is a targetable driver of prostate cancer growth
Bibliographic record
Abstract
Abstract Thyroid hormone (TH) signaling plays a major role in the development, energy homeostasis, and metabolism of most tissues. Recent studies have identified THs as drivers of prostate cancer (PCa) development and progression. We reported that the T3-scavenger protein µ-crystallin (CRYM) regulates the development and progression of PCa and that this involved crosstalk with androgen receptor (AR) signaling. However, the mechanisms remain incompletely understood. Here, we explored the role of thyroid hormone receptor β (TRβ), which is the main effector of TH signaling, in the context of PCa. The use of the TRβ-selective antagonist NH-3 inhibited PCa cell proliferation in vitro and reduced tumor size in PCa xenograft models in vivo. Notably, NH-3 was highly effective in the engrafted 22Rv1 cell line, a model for castration-resistant PCa (CRPC). Mechanistic studies revealed that NH-3 downregulates AR and the AR target genes Nkx3.1 and KLK3 (PSA). NH-3 was a more effective anticancer agent than enzalutamide, and their combined use was synergistic. Evidence from human datasets corroborates our findings, whereby elevated TRβ expression and mutations in the TH signaling pathway are associated with the onset of PCa. Collectively, these results establish TRβ as a mediator of tumorigenesis in PCa and identify NH-3 as a promising therapeutic agent for targeting AR signaling, particularly in CRPC. Graphical Abstract
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.184 | 0.021 |
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".