Disruption of the Y-Box Binding Protein-1 Results in Suppression of the Epidermal Growth Factor Receptor and HER-2
Why this work is in the frame
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Bibliographic record
Abstract
The overexpression of the epidermal growth factor receptor (EGFR) and HER-2 underpin the growth of aggressive breast cancer; still, it is unclear what governs the regulation of these receptors. Our laboratories recently determined that the Y-box binding protein-1 (YB-1), an oncogenic transcription/translation factor, induced breast tumor cell growth in monolayer and in soft agar. Importantly, mutating YB-1 at Ser(102), which resides in the DNA-binding domain, prevented growth induction. We reasoned that the underlying cause for growth attenuation by YB-1(Ser(102)) is through the regulation of EGFR and/or HER-2. The initial link between YB-1 and these receptors was sought by screening primary tumor tissue microarrays. We determined that YB-1 (n = 389 cases) was positively associated with EGFR (P < 0.001, r = 0.213), HER-2 (P = 0.008, r = 0.157), and Ki67 (P < 0.0002, r = 0.219). It was inversely linked to the estrogen receptor (P < 0.001, r = -0.291). Overexpression of YB-1 in a breast cancer cell line increased HER-2 and EGFR. Alternatively, mutation of YB-1 at Ser(102) > Ala(102) prevented the induction of these receptors and rendered the cells less responsive to EGF. The mutant YB-1 protein was also unable to optimally bind to the EGFR and HER-2 promoters based on chromatin immunoprecipitation. Furthermore, knocking down YB-1 with small interfering RNA suppressed the expression of EGFR and HER-2. This was coupled with a decrease in tumor cell growth. In conclusion, YB-1(Ser(102)) is a point of molecular vulnerability for maintaining the expression of EGFR and HER-2. Targeting YB-1 or more specifically YB-1(Ser(102)) are novel approaches to inhibiting the expression of these receptors to ultimately suppress tumor cell growth.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it