YAP activation and Hippo signaling suppression by PKC eta promote triple-negative breast cancer metastasis
Bibliographic record
Abstract
Abstract Breast cancer is the leading cause of cancer-related deaths in women, with metastasis being the primary reason for mortality. Patients with triple-negative breast cancer (TNBC) show an increased risk of metastatic dissemination. Protein kinase C eta (PKCη), an anti-apoptotic kinase of the novel PKC subfamily, is associated with poor prognosis in breast cancer patients. Here, we demonstrate that PKCη promotes metastasis in TNBC cells and show that this is mediated by the PKCƞ-YAP signaling axis. Knockout of PKCη (PKCηKO) in the TNBC cells, 4T1 and MDA-MB-231, markedly inhibited their invasion and migration capability. Furthermore, orthotopic xenografts of the latter PKCηKO cells in NSG mice reduced tumor growth and lung metastasis compared to PKCη-intact tumors. Mechanistically, we show that PKCη regulates epithelial-to-mesenchymal transition (EMT), as knockout of PKCη in TNBC cell lines increased expression of the EMT markers E-cadherin, EpCAM, and slug, and decreased expression of vimentin, ZEB1. Further profiling of the Hippo-YAP axis showed that PKCη is a negative regulator of the Hippo pathway that leads to YAP stabilization and its phosphorylation at Ser128, which allows YAP to translocate to the nucleus and contribute to metastasis of TNBC cells. We further show that PKCη directly interacts with YAP in silico and TNBC cells. Lastly, we demonstrate that treatment of TNBC cells with uPEP2, a recently discovered PKCη kinase inhibitory peptide (encoded by a uORF upstream of PKCη coding sequence), activates the Hippo pathway and YAP degradation. In summary, our results highlight the impact of PKCη in TNBC metastasis and offer a novel avenue for therapeutic intervention in this aggressive and fatal disease.
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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) | 0.007 | 0.001 |
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".