Prostate-Specific Antigen Modulates Genes Involved in Bone Remodeling and Induces Osteoblast Differentiation of Human Osteosarcoma Cell Line SaOS-2
Bibliographic record
Abstract
PURPOSE: The high prevalence of osteoblastic bone metastases in prostate cancer involves the production of osteoblast-stimulating factors by prostate cancer cells. Prostate-specific antigen (PSA) is a serine protease uniquely produced by prostate cancer cells and is an important serologic marker for prostate cancer. In this study, we examined the role of PSA in the induction of osteoblast differentiation. EXPERIMENTAL DESIGN: Human cDNA containing a coding region for PSA was transfected into human osteosarcoma SaOS-2 cells. SaOS-2 cells were also treated with exogenously added PSA. We evaluated changes in global gene expression using cDNA arrays and Northern blot analysis resulting from expression of PSA in human osteosarcoma SaOS-2 cells. RESULTS: SaOS-2 cells expressing PSA had markedly up-regulated expression of genes associated with osteoblast differentiation including runx-2 and osteocalcin compared with the controls. Consistent with these results, the stable clones expressing PSA showed increased mineralization and increased activity of alkaline phosphatase in vitro compared with controls, suggesting that these cells undergo osteoblast differentiation. We also found that osteoprotegerin expression was down-regulated and that the receptor activator of NF-kappaB ligand expression was up-regulated in cells expressing PSA compared with controls. CONCLUSIONS: Modulation of the expression of osteogenic genes and alteration of the balance between osteoprotegerin-receptor activator of NF-kappaB ligand by PSA suggests that PSA produced by metastatic prostate cancer cells may participate in bone remodeling in favor of the development of osteoblastic metastases in the heterogeneous mixture of osteolytic and osteoblastic lesions. These findings provide a molecular basis for understanding the high prevalence of osteoblastic bone metastases in 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".