<scp>LRP</scp>5 knockdown: effect on prostate cancer invasion growth and skeletal metastasis <i>in vitro</i> and <i>in vivo</i>
Bibliographic record
Abstract
Prostate cancer (PCa) is a common hormone-dependent malignancy associated with the development of skeletal metastases. This is due to the increased expression of a number of growth factors, cytokines, and proteases which collectively drive the metastatic cascade in general and increased propensity to develop skeletal metastasis in particular. While a number of signaling pathways have been implicated in PCa progression, the highly complex wnt/β-catenin pathway is unique due to its ability to regulate gene expression, cell invasion, migration, survival, proliferation, and differentiation to contribute in the initiation and progression of PCa. Members of the wnt family bind to the Frizzle proteins or lipoprotein-related receptor proteins 5, 6 (LRP5, -6) to activate this key pathway. In the current study, we have investigated the role of wnt/β-catenin pathway in PCa progression, skeletal metastasis, and gene expression using the dominant negative plasmid of LRP5 (DN-LRP5) and human PCa cells PC-3. Inactivation of LRP5 resulted in mesenchymal to epithelial shift, lack of translocation of β-catenin to cell surface, increased tumor cell proliferation, decreased colony formation, migration and invasion in vitro. These effects were attributed to decreased expression of pro-invasive and pro-metastatic genes. In in vivo studies, PC-3-DN-LRP5 cells developed significantly smaller tumors and a marked decrease in skeletal lesion area and number as determined by X-ray, micro (μ) CT and histological analysis. Collectively results from these studies demonstrate the dominant role of this key pathway in PCa growth and skeletal metastasis and its potential as a viable therapeutic target.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".