Fluid flow exposure promotes epithelial-to-mesenchymal transition and adhesion of breast cancer cells to endothelial cells
Bibliographic record
Abstract
BACKGROUND: Mechanical interactions between tumor cells and microenvironments are frequent phenomena during breast cancer progression, however, it is not well understood how these interactions affect Epithelial-to-Mesenchymal Transition (EMT). EMT is associated with the progression of most carcinomas through induction of new transcriptional programs within affected epithelial cells, resulting in cells becoming more motile and adhesive to endothelial cells. METHODS: MDA-MB-231, SK-BR-3, BT-474, and MCF-7 cells and normal Human Mammary Epithelial Cells (HMECs) were exposed to fluid flow in a parallel-plate bioreactor system. Changes in expression were quantified using microarrays, qPCR, immunocytochemistry, and western blots. Gene-gene interactions were elucidated using network analysis, and key modified genes were examined in clinical datasets. Potential involvement of Smads was investigated using siRNA knockdown studies. Finally, the ability of flow-stimulated and unstimulated cancer cells to adhere to an endothelial monolayer, migrate and invade membrane pores was evaluated in flow and static adhesion experiments. RESULTS: Fluid flow stimulation resulted in upregulation of EMT inducers and downregulation of repressors. Specifically, Vimentin and Snail were upregulated both at the gene and protein expression levels in flow stimulated HMECs and MDA-MB-231 cells, suggesting progression towards an EMT phenotype. Flow-stimulated SNAI2 was abrogated with Smad3 siRNA. Flow-induced overexpression of a panel of cell adhesion genes was also observed. Network analysis revealed genes involved in cell flow responses including FN1, PLAU, and ALCAM. When evaluated in clinical datasets, overexpression of FN1, PLAU, and ALCAM was observed in patients with different subtypes of breast cancer. We also observed increased adhesion, migration and invasion of flow-stimulated breast cancer cells compared to unstimulated controls. CONCLUSIONS: This study shows that fluid forces on the order of 1 Pa promote EMT and adhesion of breast cancer cells to an endothelial monolayer and identified biomarkers were distinctly expressed in patient populations. A better understanding of how biophysical forces such as shear stress affect cellular processes involved in metastatic progression of breast cancer is important for identifying new molecular markers for disease progression, and for predicting metastatic risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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 teacher head, 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".