Insulin receptor expression by human prostate cancers
Bibliographic record
Abstract
BACKGROUND: Although recent laboratory and population studies suggest that prostate cancer may be responsive to insulin, there is a gap in knowledge concerning the expression of insulin receptors on benign or malignant prostate tissue. METHODS: We immunostained 644 cores on tissue microarrays prepared from 29 prostate tissue samples without malignancies, 78 Gleason grade 3 cancers, 21 Gleason grade 4 cancers and 33 Gleason grade 5 cancers with antibodies against the insulin-like growth factor I receptor and the insulin receptor. RESULTS: We observed immunoreactivity with both antibodies, which implies the presence of hybrid receptors as well as IGF-I receptors and insulin receptors. Insulin receptor staining intensity was significantly (P < 0.001) higher on malignant than benign prostate epithelial cells. Analysis of information from public gene expression databases confirmed that co-expression of insulin receptor mRNA and IGF-I receptor mRNA is common in prostate cancer specimens. RT-PCR methods provided evidence for the presence of mRNA for both IR-A and IR-B insulin receptor isoforms. CONCLUSION: These observations document the presence of insulin receptors on primary human prostate cancers. The findings are relevant not only to ongoing clinical trials of drug candidates that target IGF-I and/or insulin receptors, but also to the hypothesis that obesity-associated hyperinsulinemia mediates the adverse effect of obesity on prostate cancer prognosis.
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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 | 0.001 |
| 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) | 0.004 | 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".