Complex Impacts of PI3K/AKT Inhibitors to Androgen Receptor Gene Expression in Prostate Cancer Cells
Bibliographic record
Abstract
BACKGROUND: Androgen deprivation therapy (ADT) is the first-line treatment to metastatic prostate cancer (PCa). However, sustained expression and function of the androgen receptor (AR) gene contribute to the progression of castration resistant prostate cancers (CRPC). Additionally, tumors can adapt the PI3K/AKT survival pathway to escape ADT. Co-targeting AR and PI3K/AKT signaling has been proposed to be a more effective therapeutic means for CRPC patients. Many clinical trials are ongoing to test whether PI3K/AKT inhibitors are beneficial to PCa patients. However whether these inhibitors have any impacts on the expressions of full length AR (AR-FL) and its splice variant (AR-V7) remains unclear. METHODS: Four human prostate cancer cell lines (LNCaP, LNCaP95, VCaP and 22Rv1) with different genetic backgrounds were treated with five PI3K/AKT inhibitors (LY294002, Wortmannin, BKM120, AKTi and AZD5363) and or AKT siRNA. AR and AR-V7 protein and mRNA levels were measured by immunoblotting and real-time PCR assays. AR gene transcription initiation, alternative RNA splicing and AR mRNA degradation rates were also determined. RESULTS: PI3K/AKT inhibitors had various impacts on AR protein expressions primarily through alterations of AR gene transcription initiation and RNA splicing. However, these effects remained unchanged in the presence RNA silencing of the AKT genes. CONCLUSION: PI3K/AKT inhibitors have off-target effects on AR gene expression in prostate cancer cells, which shall be considered when applying these inhibitors to PCa patients, particularly patients under ADT treatment.
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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 | 0.001 |
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".