Characterization of Precursor-Dependent Steroidogenesis in Human Prostate Cancer Models
Bibliographic record
Abstract
Castration-resistant prostate tumors acquire the independent capacity to generate androgens by upregulating steroidogenic enzymes or using steroid precursors produced by the adrenal glands for continued growth and sustainability. The formation of steroids was measured by liquid chromatography-mass spectrometry in LNCaP and 22Rv1 prostate cancer cells, and in human prostate tissues, following incubation with steroid precursors (22-OH-cholesterol, pregnenolone, 17-OH-pregnenolone, progesterone, 17-OH-progesterone). Pregnenolone, progesterone, 17-OH-pregnenolone, and 17-OH-progesterone increased C21 steroid (5-pregnan-3,20-dione, 5-pregnan-3,17-diol-20-one, 5-pregnan-3-ol-20-one) formation in the backdoor pathway, and demonstrated a trend of stimulating dihydroepiandrosterone or its precursors in the backdoor pathway in LNCaP and 22Rv1 cells. The precursors differentially affected steroidogenic enzyme messenger RNA (mRNA) expressions in the cell lines. The steroidogenesis following incubation of human prostate tissue with 17-OH-pregnenolone and progesterone produced trends similar to those observed in cell lines. Interestingly, the formation of C21 steroids from classical pathway was not stimulated but backdoor pathway steroids (e.g., 5-pregnan-3,20-dione, 5-pregnan-3-ol-20-one) were elevated following incubations with prostate tissues. Overall, C21 steroids were predominantly formed in the classical as well as backdoor pathways, and steroid precursors induced a diversion of steroidogenesis to the backdoor pathway in both cell lines and human prostate tissue, and influenced adaptive steroidogenesis to form C21 steroids.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
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".