Intracellular Expression of Inflammatory Proteins S100A8 and S100A9 Leads to Epithelial-mesenchymal Transition and Attenuated Aggressivity of Breast Cancer Cells
Bibliographic record
Abstract
S100 inflammatory proteins have been previously shown to modulate breast cancer processes. More specifically, genome-wide transcriptome studies associate S100A8 and S100A9 members to breast cancer progression and malignancy. Findings have shown that S100A8 and S100A9 can signal and regulate cancer cell behavior through both extracellular and intracellular-initiated cascades. However, functional studies exploring the effects of S100 proteins are often contradictory leaving ambiguity and a paucity of data relating to the specific function of S100A8 and S100A9 in breast cancer progression. In this study we sought to better define the functions of intracellular expressed S100A8 and S100A9 on key signaling and cellular processes driving breast cancer malignancy. We observed that extracellular treatments of the MCF7 breast cancer cell line with S100A8 and S100A9 proteins induces cell proliferation. In contrast, intracellular recombinant expression of S100A8 and S100A9 led to growth suppression. Furthermore our analysis revealed that intracellular-expressed S100A8 and S100A9 promote an epithelial-like phenotype through the induction of key markers, such as Ecadherin, integrin alpha-5 and Zona Occludens 1 (ZO-1). Concomitantly, S100A8 and S100A9 negatively regulate the activity of the promalignant Focal Adhesion Kinase-1 (FAK) signaling cascade leading to changes in cell adhesion and invasion properties. Our results uncover important differences in intracellular versus extracellular initiated S100A8 and S100A9 signaling cascades and their effects on mammary epithelial growth. Importantly, S100A8 and S100A9 appear to suppress breast cancer malignancy through an increase in mesenchymal to epithelial transitioning. Our findings shed insight into S100 protein involvement in breast cancer invasiveness and metastasis and clarify some of the controversies relating to these proteins in breast cancer processes.
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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 | 0.000 | 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 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".