An EGR2/CITED1 Transcription Factor Complex and the 14-3-3σ Tumor Suppressor Are Involved in Regulating ErbB2 Expression in a Transgenic-Mouse Model of Human Breast Cancer
Bibliographic record
Abstract
Amplification and elevated expression of the ErbB2 receptor tyrosine kinase occurs in 20% of human breast cancers and is associated with a poor prognosis. We have previously demonstrated that mammary tissue-specific expression of activated ErbB2 under the control of its endogenous promoter results in mammary tumor formation. Tumor development was associated with amplification and overexpression of ErbB2 at both the transcript and protein levels. Here we demonstrate that the EGR2/Krox20 transcription factor and its coactivator CITED1 are coordinately upregulated during ErbB2 tumor induction. We have identified an EGR2 binding site in the erbB2 promoter and demonstrated by chromatin immunoprecipitation assays that EGR2 and CITED1 associate specifically with this region of the promoter. EGR2 and CITED1 were shown to associate, and expression from an erbB2 promoter-reporter construct was stimulated by EGR2 and was further enhanced by CITED1 coexpression. Furthermore, expression of the 14-3-3sigma tumor suppressor led to downregulation of ErbB2 protein levels and relocalization of EGR2 from the nucleus to the cytoplasm. Taken together, these observations suggest that, in addition to an increased gene copy number and upregulation of EGR2 and CITED1, an elevated erbB2 transcript level involves the loss of 14-3-3sigma, which sequesters a key transcriptional regulator of the erbB2 promoter.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".