HER2 induces cell scattering and invasion through ∆Np63α and E-cadherin
Bibliographic record
Abstract
Human epidermal growth factor receptor 2 (HER2)-positive breast cancer constitutes approximately 30% of human breast cancers and is associated with poor outcomes. ∆Np63 is considered a metastasis inhibitor involved with cancer progression. This study aimed to clarify the roles and mechanisms of HER2 and ∆Np63 on scattering and invasion of MCF10A cells. Wild-type or mutant HER2 was cloned and transfected into MCF10A cells. Cell counting and transwell assays were applied to unveil the impact of HER2 upregulation and mutation on proliferation, cell scattering, and invasion. Western blotting and cell accounting were used to investigate the roles of ∆Np63 and p27. In vivo lung colonization assay was used to reveal the influences of HER2 and ∆Np63a on tumor metastasis. The results indicated HER2 remarkably enhanced cell proliferation, invasion, and scattering. Overexpression of either ΔNp63 or E-cadherin led to attenuated HER2-mediated regulation of cell migration, invasion, and scattering. Furthermore, we confirmed that HER2 enhanced cell proliferation but not migration through p27 and independent ∆Np63a. The results revealed that ∆Np63α contributed to the inhibition of HER2-induced metastasis. Collectively, our findings may further our understanding of the regulation of tumor progression and metastasis.
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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".