Dipeptidyl-peptidase 9 regulates the dynamics of tumorigenesis and metastasis in breast cancer
Bibliographic record
Abstract
The cytosolic dipeptidyl-aminopeptidase 9 (DPP9) cleaves protein N-termini post-proline or -alanine. Our analysis of DPP9 mRNA expression from the TCGA 'breast cancer' data set revealed that low/intermediate DPP9 levels are associated with poor overall survival of breast cancer patients. To unravel the impact of DPP9 on breast cancer development and progression, the transgenic MMTV-PyMT mouse model of metastasizing breast cancer was used. In addition, tissue- and time-controlled genetic deletion of DPP9 by the Cre-loxP recombination system was done. Despite a delay of tumor onset, a higher number of lung metastases were measured in DPP9-deficient mice compared to controls. In human mammary epithelial cells with oncogenic RAS pathway activation, DPP9 deficiency delayed tumorigenic transformation and accelerated TGF-β1 induced epithelial-to-mesenchymal transition (EMT) of spheroids. For further analysis of the mechanism, primary breast tumor cells were isolated from the MMTV-PyMT model. DPP9 deficiency in these cells caused cancer cell migration and invasion accompanied by EMT. In absence of DPP9, the EMT transcription factor ZEB1 was stabilized due to insufficient degradation by the proteasome. In summary, low expression of DPP9 appears to decelerate mammary tumorigenesis but favors EMT and metastasis, which establishes DPP9 as a novel dynamic regulator of breast cancer initiation and progression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".