Cyclin D1 Is Required for Transformation by Activated Neu and Is Induced through an E2F-Dependent Signaling Pathway
Bibliographic record
Abstract
The neu (c-erbB-2) proto-oncogene encodes a tyrosine kinase receptor that is overexpressed in 20 to 30% of human breast tumors. Herein, cyclin D1 protein levels were increased in mammary tumors induced by overexpression of wild-type Neu or activating mutants of Neu in transgenic mice and in MCF7 cells overexpressing transforming Neu. Analyses of 12 Neu mutants in MCF7 cells indicated important roles for specific C-terminal autophosphorylation sites and the extracellular domain in cyclin D1 promoter activation. Induction of cyclin D1 by NeuT involved Ras, Rac, Rho, extracellular signal-regulated kinase, c-Jun N-terminal kinase, and p38, but not phosphatidylinositol 3-kinase. NeuT induction of the cyclin D1 promoter required the E2F and Sp1 DNA binding sites and was inhibited by dominant negative E2F-1 or DP-1. Neu-induced transformation was inhibited by a cyclin D1 antisense or dominant negative E2F-1 construct in Rat-1 cells. Growth of NeuT-transformed mammary adenocarcinoma cells in nude mice was blocked by the cyclin D1 antisense construct. These results demonstrate that E2F-1 mediates a Neu-signaling cascade to cyclin D1 and identify cyclin D1 as a critical downstream target of neu-induced transformation.
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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".