Osteoblast-Derived Factors Induce an Expression Signature that Identifies Prostate Cancer Metastasis and Hormonal Progression
Bibliographic record
Abstract
Identification of gene expression signatures associated with metastases provides a tool to discern mechanisms and potential therapeutic targets and may lead toward a molecular classification system in pathology. Prostate cancer (CaP) frequently metastasizes to the bone to form osteoblastic lesions. Correlative clinical data and in vitro evidence have led to the hypothesis that osteoblast-derived factors promote hormonal progression of CaP cells. Here, the gene expression signature of CaP exposed to osteoblast-derived factors was identified. This signature included known androgen-regulated genes, oncogenes, tumor suppressors, and genes whose products are involved in apoptosis and cell cycle. A comparative functional genomic approach involved the application of this responsive gene expression signature to clinical samples of human CaP, melanomas, and oral cancers. Cluster analysis revealed that this gene expression signature had specificity for CaP and could resolve clinical specimens according to stage (benign, localized, and metastatic) and androgen sensitivity with an accuracy of 100% and 80%, respectively. Together, these results suggest that factors derived from osteoblasts induce a more advanced phenotype of CaP and promotes hormonal 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 | 0.000 |
| 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".