Intratumoral Sterol-27-Hydroxylase (<i>CYP27A1</i>) Expression in Relation to Cholesterol Synthesis and Vitamin D Signaling and Its Association with Lethal Prostate Cancer
Bibliographic record
Abstract
Abstract Background: Higher intratumoral cholesterol synthesis is associated with a worse prognosis in prostate cancer. The vitamin D–regulated enzyme sterol-27-hydroxylase (CYP27A1) converts cholesterol to 27-hydroxycholesterol, potentially lowering intracellular cholesterol levels. We hypothesized that low CYP27A1 expression is associated with high cholesterol synthesis, low vitamin D signaling, and higher risk of lethal prostate cancer. Methods: In 404 patients from the prospective prostate cancer cohorts within the Health Professionals Follow-up Study (HPFS) and the Physicians’ Health Study (PHS), we assessed intratumoral CYP27A1 expression and proxies of cholesterol synthesis using transcriptome profiling, prediagnostic plasma 25-hydroxyvitamin D [25(OH)D; n = 132], and intratumoral vitamin D receptor protein expression (VDR; n = 300). Patients were followed for metastases and prostate cancer mortality (lethal cancer; median follow-up, 15.3 years). Results: CYP27A1 expression was lower in tumors with higher Gleason grade and higher expression of cholesterol synthesis enzymes, including the second rate-limiting enzyme, SQLE. We did not detect consistent associations between CYP27A1 and 25(OH)D, VDR, or CYP24A1 mRNA expression. Lower CYP27A1 was associated with higher risk of lethal cancer in both cohorts, independent of SQLE [adjusted OR for lowest vs. highest quartile of CYP27A1, 2.64; 95% confidence interval (CI), 1.24–5.62]. This association was attenuated when additionally adjusting for Gleason grade (OR, 1.76; 95% CI, 0.75–4.17). Conclusions: Low CYP27A1 expression was associated with higher cholesterol synthesis and a higher risk of lethal disease. Impact: These observations further support the hypothesis that intratumoral cholesterol accumulation through higher synthesis and decreased catabolism is a feature of lethal prostate cancer.
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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".