Increased Insulin-Like Growth Factor I Receptor Expression and Signaling Are Components of Androgen-Independent Progression in a Lineage-Derived Prostate Cancer Progression Model
Bibliographic record
Abstract
Apoptosis and inhibition of mitosis are primary mechanisms mediating androgen ablation therapy-induced regression of prostate cancer (PCa). However, PCa readily becomes androgen independent, leading to fatal disease. Up-regulated growth and survival signaling is implicated in development of resistance to androgen ablation therapy. We are testing the hypothesis that insulin-like growth factor (IGF) responsiveness is required for androgen-independent (AI) progression. Using the LNCaP human PCa progression model, we have determined that IGF-I-mediated protection from apoptotic stress and enhanced mitotic activity is androgen dependent in LNCaP cells but is androgen independent in lineage-derived C4-2 cells. Both cell lines exhibit androgen-responsive patterns of IGF-I receptor (IGF-IR) expression, activation, and signaling to insulin receptor substrate-2 and AKT. However, C4-2 cells express higher levels of IGF-IR mRNA and protein and exhibit enhanced IGF-I-mediated phosphorylation and downstream signaling under androgen-deprived conditions. In comparisons of naive and AI metastatic human PCa specimens, we have confirmed that IGF-IR levels are elevated in advanced disease. Together with our LNCaP/C4-2 AI progression model data, these results indicate that increased IGF-IR expression is associated with AI antiapoptotic and promitotic IGF signaling in PCa disease progression.
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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 | 0.001 | 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".