The S100A7-c-Jun Activation Domain Binding Protein 1 Pathway Enhances Prosurvival Pathways in Breast Cancer
Bibliographic record
Abstract
S100A7 is among the most highly expressed genes in preinvasive breast cancer, is a marker of poor survival when expressed in invasive disease, and promotes breast tumor progression in experimental models. To explore the mechanism of action, we examined the role of S100A7 in cell survival and found that overexpression of S100A7 in MDA-MB-231 cell lines promotes survival under conditions of anchorage-independent growth. This effect is paralleled by increased activity of nuclear factor-kappaB (3-fold) and phospho-Akt (4-fold), which are known to mediate prosurvival pathways. S100A7 and phospho-Akt are also correlated in breast tumors examined by immunohistochemistry (n = 142; P < 0.0001; r = 0.34). To explore the underlying mechanism, we examined the role of a putative c-Jun activation domain-binding protein 1 (Jab1)-binding domain within S100A7 using a panel of MDA-MB-231 breast cell lines stably transfected with either S100A7 or S100A7 mutated at the Jab1 domain. Structural analysis by three-dimensional protein modeling, immunoprecipitation, and yeast two-hybrid assay and functional analysis using transfected reporter gene and Western blot assays revealed that the in vitro effects of S100A7 on phospho-Akt and the nuclear factor-kappaB pathway are dependent on the Jab1-binding site and the interaction with Jab1. Enhanced epidermal growth factor receptor signaling was also found to correlate with the increased phospho-Akt. Furthermore, the Jab1-binding domain is also necessary for the enhanced tumorigenicity conferred by S100A7 expression in murine xenograft tumors in vivo. We conclude that the S100A7-Jab1 pathway acts to enhance survival under conditions of cellular stress, such as anoikis, which may promote progression of breast cancer.
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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) | 0.001 | 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".