CACNA1D overexpression and voltage-gated calcium channels in prostate cancer during androgen deprivation
Bibliographic record
Abstract
Abstract Prostate cancer is often treated by perturbing androgen receptor signalling. CACNA1D, encoding CaV1.3 ion channels is upregulated in prostate cancer. Here we show how hormone therapy affects CACNA1D expression and CaV1.3 function. Human prostate cells (LNCaP, VCaP, C4-2B, normal RWPE-1) and a tissue microarray were used. Cells were treated with anti-androgen drug, Enzalutamide (ENZ) or androgen-removal from media, mimicking androgen-deprivation therapy (ADT). Proliferation assays, qPCR, Western blot, immunofluorescence, Ca2+-imaging and patch-clamp electrophysiology were performed. Nifedipine, Bay K 8644 (CaV1.3 inhibitor, activator), mibefradil, Ni2+ (CaV3.2 inhibitors) and high K+ depolarising solution were employed. CACNA1D and CaV1.3 protein are overexpressed in prostate tumours and CACNA1D was overexpressed in androgen-sensitive prostate cancer cells. In LNCaP, ADT or ENZ increased CACNA1D time-dependently whereas total protein showed little change. Untreated LNCaP were unresponsive to depolarising high K+/Bay K (to activate CaV1.3); moreover, currents were rarely detected. ADT or ENZ-treated LNCaP exhibited nifedipine-sensitive Ca2+-transients; ADT-treated LNCaP exhibited mibefradil-sensitive or, occasionally, nifedipine-sensitive inward currents. CACNA1D knockdown reduced the subpopulation of treated-LNCaP with CaV1.3 activity. VCaP displayed nifedipine-sensitive high K+/Bay K transients (responding subpopulation was increased by ENZ), and Ni2+-sensitive currents. Hormone therapy enables depolarization/Bay K-evoked Ca2+-transients and detection of CaV1.3 and CaV3.2 currents. Physiological and genomic CACNA1D/CaV1.3 mechanisms are likely active during hormone therapy—their modulation may offer therapeutic advantage.
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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".