Profiling changes in the metastatic potential of breast cancer cells exposed to flow
Bibliographic record
Abstract
Most cancer related deaths can be directly attributed to blood bourne metastasis.Metastasis is the process by which tumor cells leave the initial tumor travel through thecirculatory or lymphatic system to distant sites where secondary tumors are formed.One important part of metastasis is epithelial to mesenchymal transition (EMT), inwhich cells lose their epithelial cell morphology and gain a mesenchymal morphology.This transition causes enhanced migratory capacity, invasiveness, and resistance tocell death, creating a more metastatic cell. In this study we used a parallel plate flowchamber to create conditions that would encourage EMT in breast cancer cells. Severalbreast cancer cell lines were exposed to high shear stress (10 dyn/cm2) for 20 hours.RNA was collected from static and flow exposed cells. RT-qPCR was used to comparethe expression of four genes known to be related to EMT or cell invasiveness. Wefound that TSP-1 gene expression was strongly upregulated, MMP-14 was slightlyupregulated, ICAM-1 was slightly downregulated, and TGFR1 gene expression didnot change. TSP-1 has been shown to increase tumor cell migration and invasivenessand MMP-14 has also been associated with increased tumor cell invasiveness. ICAM-1is an intercellular adhesion molecule that plays different roles in cell to cell adhesion.The loss of ICAM-1 and the up regulation of TSP-1 and MMP-14 show that the cellsexposed to flow lose some intercellular interaction and gain increased mobility andinvasiveness, all of which is indicative of EMT. These results show for the first timethat fluid forces can upregulate genes involved in cancer cell EMT. This will be used infuture studies to investigate more about the metastatic potential of breast cancer cells.11
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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".