GLI2 Knockdown Using an Antisense Oligonucleotide Induces Apoptosis and Chemosensitizes Cells to Paclitaxel in Androgen-Independent Prostate Cancer
Bibliographic record
Abstract
PURPOSE: GLI transcription factors mediate hedgehog signaling and have been implicated in several human malignancies, including prostate cancer. The objectives of this study were to characterize GLI2 expression levels in human prostate cancer cell lines and tissues to test the effect of antisense oligonucleotide (ASO) targeting GLI2 on androgen-independent (AI) prostate cancer cell lines. EXPERIMENTAL DESIGN: A tissue microarray was used to characterize differences in GLI2 expression in benign prostate hyperplasia, prostate cancer treated by neoadjuvant hormonal therapy and AI prostate cancer. The effects of GLI2 ASO on PC-3 cell growth and paclitaxel chemosensitivity were assessed in vitro and in vivo. Oligonucleotide spotted microarray analysis was used to determine alteration in GLI2 coregulated genes after ASO treatment. RESULTS: The expression of GLI2 was significantly higher in prostate cancer than in benign prostate hyperplasia, decreased after androgen ablation in a time-dependent fashion, but became highly expressed again in AI prostate cancer. GLI2 ASO treatment of PC-3 cells reduced GLI2 mRNA and protein levels in a dose-dependent manner. GLI2 knockdown increased PC-3 cell apoptotic rates and significantly decreased cell growth and modulated levels of apoptosis-related genes, such as Bcl2, Bcl-xL, and clusterin. GLI2 knockdown also changed levels of several cell cycle regulators, such as cyclin D1, p27, and PKC-eta. Systematic administration of GLI2 ASO in athymic mice significantly delayed PC-3 tumor progression and enhanced paclitaxel chemosensitivity. CONCLUSIONS: These findings suggest that increased levels of GLI2 correlates with AI progression and that GLI2 may be a therapeutic target in castrate-resistant prostate cancer.
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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) | 0.001 | 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".