Disruption of the Y-Box Binding Protein-1 Results in Suppression of the Epidermal Growth Factor Receptor and HER-2
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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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".