Estradiol propionate reduction of nuclear size in prostate cancer lines lacking DHRS7 suggests DHRS7 absence as a biomarker for the effectiveness of estrogen therapy
Bibliographic record
Abstract
ABSTRACT Increased nuclear size correlates with lower survival rates for prostate cancer and is a hallmark of late-stage androgen-insensitive tumors. The short-chain dehydrogenase/reductase (SDR) family member DHRS7 was suggested as a marker for prostate cancer grading because it is lost in late-stage androgen-insensitive tumors. Here we find that loss of DHRS7 from the early-stage LNCaP prostate cancer cell line increases nuclear size, potentially explaining the nuclear size increase observed in higher-grade prostate tumors. Exogenous expression of DHRS7 in the late-stage PC3 prostate cancer cell line correspondingly decreases nuclear size. We separately tested 80 compounds from the Microsource Spectrum library for their ability to restore normal nuclear size to PC3 cells, finding estradiol propionate had the same effect as re-expression of DHRS7 in the PC3 cells. However, the drug had no effect on LNCaP cells or PC3 cells re-expressing DHRS7. We speculate that reported beneficial effects of estrogens in late-stage prostate cancer may target a pathway which is only active in cells lacking DHRS7 that have increased nuclear size and propose DHRS7 as a potential biomarker for the likely effectiveness of estrogen-based treatments.
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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) | 0.005 | 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".