Role of S100A8 and S100A9 proteins on breast cancer aggressivity
Bibliographic record
Abstract
Women are more affected by breast cancers than any other type of cancer. Studies show that proteins from the S100 family are differentially expressed in cancers and autoimmune disease. The S100 protein family is the largest family of calcium binding proteins where most members are overexpressed in certain types of cancers. More specifically, S100A8 and S100A9 seem to be overexpressed in mammary ductal carcinomas . Nevertheless, little is known on the cellular effects of S100 proteins and their roles in breast cancer pathogenesis. In this study, we assessed cancerous biological processes (proliferation, apoptosis, cell adherence and ECM invasion) in mammary cells following: 1) the treatment with various concentrations of recombinant and purified S100A8 and S100A9 proteins and, 2) the knocked‐up or knocked‐down expression of the S100A8 and S100A9 genes. We found that extracellular treatments with S100 proteins affected growth rates of cancer cells. We also observed that the conditioned expression of S100 protein in breast cancer cells affected cancer processes in a cell type dependent manner. Details from these results will be discussed here. These results will aid to a better understand the roles of S100A8 and S100A9 proteins in epithelial cell biology and breast cancer pathogenesis. The outcome of these experiments could potentially lead to a better diagnostics or more specialized treatments of breast cancers.
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 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".