IGF2 increases de novo steroidogenesis in prostate cancer cells
Bibliographic record
Abstract
IGF2 is a mitogenic foetal growth factor commonly over-expressed in cancers, including prostate cancer (PC). We recently demonstrated that insulin can activate de novo steroidogenesis in PC cells, a major pathway for reactivation of androgen pathways and PC progression. IGF2 can activate the IGF1 receptor (IGF1R) or insulin receptor (INSR) or hybrids of these two receptors. We therefore hypothesized that IGF2 may contribute to PC progression via de novo steroidogenesis. IGF2 mRNA but not IGF2 receptor mRNA expression was increased in patient samples during progression to castrate-resistant PC as was immunoreactivity to INSR and IGF1R antibodies. Treatment of androgen receptor (AR)-positive PC cell lines LNCaP and 22RV1 with IGF2 for 48 h resulted in increased expression of steroidogenic enzyme mRNA and protein, including steroid acute regulatory protein (StAR), cytochrome p450 family member (CYP)17A1, aldo-keto reductase family member (AKR)1C3 and hydroxysteroid dehydrogenase (HSD)17B3. IGF2 treatment resulted in increased steady state steroid levels and increased de novo steroidogenesis resulting in AR activation as demonstrated by PSA mRNA induction. Inhibition of the IGF1R/INSR signalling axis attenuated the effects of IGF2 on steroid hormone synthesis. We present a potential mechanism for prostatic IGF2 contributing to PC progression by inducing steroidogenesis and that IGF2 signalling and related pathways present attractive targets for PC 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".