Fluid Flow Stimulation Modulates Expression of S100 Genes in Normal Breast Epithelium and Breast Cancer
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: S100 proteins are intracellular calcium ion sensors that participate in cellular processes, some of which are involved in normal breast functioning and breast cancer development. Despite several S100 genes being overexpressed in breast cancer, their roles during disease development remain elusive. Human mammary epithelial cells (HMECs) can be exposed to fluid shear stresses and implications of such interactions have not been previously studied. The goal of this study was to analyze expression profiles of S100 genes upon exposing HMECs to fluid flow. METHODS: HMECs and breast cancer cell lines were exposed to fluid flow in a parallel-plate bioreactor system. Changes in gene expression were quantified using microarrays and qPCR, gene-gene interactions were elucidated using network analysis, and key modified genes were examined in three independent clinical datasets. RESULTS: S100 genes were among the most upregulated genes upon flow stimulation. Network analysis revealed interactions between upregulated transcripts, including interactions between S100P, S100PBP, S100A4, S100A7, S100A8 and S100A9. Overexpression of S100s was also observed in patients with early stage breast cancer compared to normal breast tissue, and in most breast cancer patients. Finally, survival analysis revealed reduced survival times for patients with elevated expression of S100A7 and S100P. CONCLUSION: This study shows that exposing HMECs to fluid flow upregulates genes identified clinically to be overexpressed during breast cancer development, including S100A7 and S100P. These findings are the first to show that S100 genes are flow-responsive and might be participating in a fundamental adaptation pathway in normal tissue that is also active in breast cancer.
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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 | 0.000 | 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) | 0.000 | 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 it