Inhibitor of DNA Binding 2 Inhibits Epithelial-Mesenchymal Transition via Up-Regulation of Notch3 in Breast Cancer
Bibliographic record
Abstract
Breast cancer is the second leading cause of cancer death in women worldwide. Incurable metastatic breast disease presents a major clinical challenge and is the main cause of breast cancer-related death. The epithelial-mesenchymal transition (EMT) is a critical early promoter of metastasis. In the present study, we identified a novel role for the inhibitor of DNA binding 2 (Id2), a member of the basic helix-loop-helix protein family, during the EMT of breast cancer. Expression of Id2 was positively correlated with Notch3 in breast cancer cells. Low expression of Id2 and Notch3 was associated with worse distant metastasis-free survival in breast cancer patients. The present study revealed that Id2 activated Notch3 expression by blocking E2A binding to an E-box motif in the Notch3 promoter. The Id2-mediated up-regulation of Notch3 expression at both the mRNA and protein levels resulted in an attenuated EMT, which was associated with reduced motility and matrix invasion of ER-positive and -negative human breast cancer cells and the emergence of E-cadherin expression and reduction in the mesenchymal marker vimentin in triple-negative breast cancer cells. In summary, our findings identified Id2 as a suppressor of the EMT and positive transcriptional regulator of Notch3 in breast cancer. Id2 and Notch3 may serve as novel prognostic markers in a subpopulation of ER-positive breast cancer patients.
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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".