Mechanically stimulated osteocytes reduce the bone‐metastatic potential of breast cancer cells in vitro by signaling through endothelial cells
Bibliographic record
Abstract
Bone metastases occur in 65% to 75% of patients with advanced breast cancer and significantly worsen their survival and quality of life. We previously showed that conditioned medium (CM) from osteocytes stimulated with oscillatory fluid flow, mimicking bone mechanical loading during routine physical activities, reduced the transendothelial migration of breast cancer cells. Endothelial cells are situated at an ideal location to mediate signals between osteocytes in the bone matrix and metastasizing cancer cells in the blood vessels. In this study, we investigated the specific effects of flow-stimulated osteocytes on the interaction between endothelial cells and breast cancer cells in vitro. We observed that CM from flow-stimulated osteocytes reduced endothelial permeability by 15% and breast cancer cell adhesion onto endothelial monolayers by 18%. The difference in adhesion was abolished with anti-intercellular adhesion molecule 1 (ICAM-1) neutralizing antibodies. Furthermore, CM from endothelial cells conditioned in CM from flow-stimulated osteocytes significantly altered the gene expression in bone-metastatic breast cancer cells, as shown by RNA sequencing. Specifically, breast cancer cell expression of matrix metallopeptidase 9 (MMP-9) was downregulated by 62%, and frizzled-4 (FZD4) by 61%, when the osteocytes were stimulated with flow. The invasion of these breast cancer cells across Matrigel was also reduced by 47%, and this difference was abolished by MMP-9 inhibitors. In conclusion, we demonstrated that flow-stimulated osteocytes downregulate the bone-metastatic potential of breast cancer cells by signaling through endothelial cells. This provides insights into the capability of bone mechanical regulation in preventing bone metastases; and may assist in prescribing exercise or bone-loading regimens to patients with breast cancers.
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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) | 0.002 | 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".