The Androgen Receptor and MYC synergise to modulate the synthesis of Siglec-7 ligands in prostate cancer
Bibliographic record
Abstract
Abstract Glyco-immune checkpoints have recently been shown to be critical mediators of immunotherapy resistance across multiple cancer types. In clinical trials, immunotherapeutic treatments for prostate cancer have failed to elicit durable clinical responses. PCa progression is driven by transcriptional networks regulated by key transcription factors including the androgen receptor (AR) and the oncogene MYC. How this crossover between hormone and oncogene-driven signalling pathways regulates tumour glyco-immune checkpoints remains unclear. Here, we show that O -glycans are the major substrates for sialylation in prostate cancer and that sialyltransferases that have preferences for O -glycans are differentially regulated by androgens. We show that supraphysiological levels of androgens produce distinct glycopeptide profiles in prostate cancer cells compared with cells exposed to physiological androgens. Additionally, we identify a direct and coordinated role for AR and MYC in regulating ST3Gal1 and the synthesis of Siglec-7 ligands in prostate cancer. Both transcription factors converge to repress ST3GAL1 , thereby limiting the generation of Siglec-7 ligands. These findings highlight a context-dependent, cooperative relationship between the AR and MYC in shaping the tumour sialome, linking hormonal signalling and oncogenic transcription to Siglec biology. Our study highlights how cell-type specific differences in transcriptional networks has important downstream effects for immune modulating glycans and has tumour specific clinical implications.
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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) | 0.002 | 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".