Tyrosine Phosphorylation of Grb2: Role in Prolactin/Epidermal Growth Factor Cross Talk in Mammary Epithelial Cell Growth and Differentiation
Bibliographic record
Abstract
Characterizing mechanisms regulating mammary cell growth and differentiation is vital, as they may contribute to breast carcinogenesis. Here, we examine a cross talk mechanism(s) downstream of prolactin (PRL), a primary differentiation hormone, and epidermal growth factor (EGF), an important proliferative factor, in mammary epithelial cell growth and differentiation. Our data indicate that EGF exerts inhibitory effects on PRL-induced cellular differentiation by interfering with Stat5a-mediated gene expression independent of the PRL-proximal signaling cascade. Additionally, our data show that PRL is a potent inhibitor of EGF-induced cell proliferation. We identify tyrosine phosphorylation of the growth factor receptor-bound protein 2 (Grb2) as a critical mechanism by which PRL antagonizes EGF-induced cell proliferation by attenuating the activation of the Ras/mitogen-activated protein kinase (MAPK) pathway. Together, our results define a novel negative cross-regulation between PRL and EGF involving the Jak2/Stat5a and Ras/MAPK pathways through tyrosine phosphorylation of Grb2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".