Bone Microenvironment-related Growth Factors, Zoledronic Acid and Dexamethasone Differentially Modulate PTHrP Expression in PC-3 Prostate Cancer Cells
Bibliographic record
Abstract
Bone metastasis microenvironment-related growth factors such as insulin-like growth factor 1 (IGF-1), transforming growth factor beta 1 (TGF-beta1), basic fibroblast growth factor (bFGF) and interleukin 6 (IL-6) show survival factor activity, thereby inhibiting chemotherapy-induced apoptosis of PC-3 prostate cancer cells in vitro. Recently, zoledronic acid has been shown to induce apoptosis in PC-3 prostate cancer cells while overexpression of parathyroid hormone-related protein (PTHrP) inhibits serum deprivation-induced apoptosis in PC-3 cells. Consequently, we have investigated whether IGF-1, TGF-beta1, bFGF, IL-6, zoledronic acid and/or dexamethasone affect the expression of the PTHrP and type I PTH/PTHrP receptor (PTH.1R) in PC-3 prostate cancer cells using relative quantitative PCR and real-time PCR (expression at mRNA level) and immunocytochemical and immunofluorescence analysis (expression at protein level). Our data show that IGF-1, TGF-beta1, bFGF and IL-6 increase PTHrP mRNA expression and its perinuclear localization, while zoledronic acid (50 muM, 100 muM for 24 h and 48 h) and dexamethasone suppress PTHrP expression in PC-3 cells. We did not detect any appreciable change of the PTH.1R expression due to IGF-1, TGF- beta1, bFGF, IL-6, zoledronic acid or dexamethasone in PC-3 cells. Therefore, it is conceivable that bone metastasis microenvironment-related survival factor/anti-apoptotic activity and zoledronic acid anticancer action/pro-apoptotic activity on PC-3 cells is mediated, at least in part, by differential modulation of PTHrP expression.
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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".