The Neurodevelopmental Protein POGZ Suppresses Metastasis in Triple-Negative Breast Cancer by Attenuating TGFβ Signaling
Bibliographic record
Abstract
The pogo transposable element-derived zinc finger protein, POGZ, is notably associated with neurodevelopmental disorders through its role in gene transcription. Many proteins involved in neurological development are often dysregulated in cancer, suggesting a potential role for POGZ in tumor biology. Here, we provided experimental evidence that POGZ influences the growth and metastatic spread of triple-negative breast cancers (TNBC). In well-characterized models of TNBC, POGZ exerted a dual role, both as a tumor promoter and metastasis suppressor. Mechanistically, loss of POGZ potentiated TGFβ pathway activation to exert cytostatic effects while simultaneously increasing the mesenchymal and migratory properties of breast tumors. Although POGZ levels are elevated in human breast cancers, the most aggressive forms of TNBC tumors, including those with increased mesenchymal and metastatic properties, exhibit dampened POGZ levels, and low POGZ expression was associated with inferior clinical outcomes in these tumor types. Taken together, these data suggest that POGZ is a critical suppressor of the early stages of the metastatic cascade. Significance: The POGZ neurodevelopmental protein plays dual functions in triple-negative breast cancers as a tumor promoter and metastasis suppressor, inhibiting TGFβ-regulated EMT to limit breast cancer metastatic progression.
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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".