Methyl-CpG binding domain protein 2 (Mbd2) drives breast cancer progression through the modulation of epithelial-to-mesenchymal transition
Bibliographic record
Abstract
Abstract Methyl-CpG-binding domain protein 2 (Mbd2), a reader of DNA methylation, has been implicated in different types of malignancies, including breast cancer. However, the exact role of Mbd2 in various stages of breast cancer growth and progression in vivo has not been determined. To test whether Mbd2 plays a causal role in mammary tumor growth and metastasis, we performed genetic knockout (KO) of Mbd2 in MMTV-PyMT transgenic mice and compared mammary tumor progression kinetics between the wild-type (PyMT-Mbd2+/+) and KO (PyMT-Mbd2−/−) groups. Our results demonstrated that deletion of Mbd2 in PyMT mice impedes primary tumor growth and lung metastasis at the experimental endpoint (postnatal week 20). Transcriptomic and proteomic analyses of primary tumors revealed that Mbd2 deletion abrogates the expression of several key determinants involved in epithelial-to-mesenchymal transition, such as neural cadherin (N-cadherin) and osteopontin. Importantly, loss of the Mbd2 gene impairs the activation of the PI3K/AKT pathway, which is required for PyMT-mediated oncogenic transformation, growth, and survival of breast tumor cells. Taken together, the results of this study provide a rationale for further development of epigenetic therapies targeting Mbd2 to inhibit the progression of breast cancer.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".