YB‐1 is upregulated during prostate cancer tumor progression and increases P‐glycoprotein activity
Bibliographic record
Abstract
BACKGROUND: Currently, the main obstacle to curing advanced prostate cancer is development of androgen independence (AI), where malignant cells acquire the ability to survive in the absence of androgens. Our initial experimental approach used cDNA microarrays to characterize changes in gene expression in the LNCaP human prostate tumor model during progression to AI. The transcription factor Y-box binding protein (YB-1) was shown to be one of the genes upregulated. We focused on increased YB-1 expression during progression in clinical specimens, and further examined one of its downstream targets, P-glycoprotein (P-gp). METHODS: Northern blot analysis was performed on LNCaP tumor series, as well as immunohistochemical analyses of human prostate cancer tissue samples. YB-1 was transiently transfected and transport analysis were performed to analyze P-gp efflux activity. RESULTS: YB-1 expression is markedly increased during benign to malignant transformation and further following androgen ablation. In addition, increased YB-1 expression after castration in the LNCaP model is linked to upregulation of P-gp. We demonstrate that YB-1 upregulates P-gp activity resulting in a 40% intracellular decrease in the P-gp substrate vinblastine. We have also found that P-gp increases the efflux of the endogenous androgen, dihydrotestosterone (DHT), from prostate cells and leads to decreased androgen regulated gene expression. CONCLUSIONS: We hypothesize that early in prostate cancer progression, increased expression of YB-1 may increase P-gp activity which may in turn lower androgen levels in the prostate tumor cells. Suppression of androgen levels may activate cell survival pathways and lead to an adaptive survival advantage of androgen independent prostate cancer cells following androgen ablation therapy.
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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) | 0.002 | 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".