EGF and S100A7 interact to regulate Jab1 in breast cancer and may influence progression of DCIS and early breast cancer
Bibliographic record
Abstract
3929 Ductal carcinoma in situ (DCIS) is a key step that precedes the earliest stages of tumor cells spreading in breast cancer. DCIS represents the ideal target in a strategy to prevent progression to invasive breast cancer. However, the biology of DCIS is relatively poorly understood. S100A7 is a Ca2 + - binding protein, which is one of the most highly expressed genes in DCIS, and is believed to influence tumor progression through interaction with Jab1 and stimulation of Jab1 related activities. The epidermal growth factors (EGF) have also been shown to be important in normal and breast cancer biology, exerting their effects through tyrosine kinase growth factor receptors. These receptors, including epidermal growth factor receptor (EGFR), S100A7, and Jab1 are frequently expressed in DCIS and associated with the same estrogen receptor negative (ER-ve) tumor phenotype. To investigate the possible interaction of S100A7-Jab1 and EGFR sigalling pathways in breast cancer progression, we examined these pathways in breast cancer cell lines MDA-MB-231, MDA-MB-231-FD3 (S100A7 transfected), and MDA-MD-468. EGF increased S100A7 expression in MDA-MB-231-FD3 and MDA-MD-468 cell lines compared to MDA-MB-231 and also increases Jab1 translocation from cytoplasm to nuclear cellular compartments. The effect of EGF on S100A7 occurs predominantly at the level of protein levels and correlates with the relative levels of EGFR in these cell lines. Inhibition of EGFR by EGF receptor kinase inhibitor, AG 1478, also reduced S100A7 expression and blocked Jab1 redistribution to nucleus. Jab1 redistribution was also prevented by pretreatment with PD98059, an ERK pathway inhibitor. Our results show that EGF activation of EGFR regulates Jab1 and that this pathway interacts with the S100A7-Jab1 pathway through both regulation of S100A7 and synergistic effects on Jab1. Exploration of the relevance of these observations in breast cancer tissues is in progress. We conclude that the interaction between the EGFR and S100A7-Jab1 pathways which are both prevalent in DCIS, may play an important role in early breast cancer progression. Understanding these interactions may have an important role in the development of novel anticancer targeted therapies in breast cancer progression in DCIS.
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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".