Proprotein convertase subtilisin/kexin Type 9 is required for Ahnak-mediated metastasis of melanoma into lung epithelial cells
Bibliographic record
Abstract
Previously we demonstrated that Ahnak mediates transforming growth factor-β (TGFβ)-induced epithelial-mesenchymal transition (EMT) during tumor metastasis. It is well-known that circulating tumor cells (CTCs) invade the vasculature of adjacent target tissues before working to adapt to the host environments. Currently, the molecular mechanism by which infiltrated tumor cells interact with host cells to survive within target tissue environments is far from clear. Here, we show that Ahnak regulates tumor metastasis through PCSK9 expression. To validate the molecular function of Ahnak in metastasis, B16F10 melanoma cells were injected into WT and Ahnak knockout (Ahnak−/−) mice. Ahnak−/− mice were more resistant to the pulmonary metastasis of B16F10 cells compared to wild-type (WT) mice. To investigate the host function of Ahnak in recipient organs against metastasis of melanoma cells, transcriptomic analyses of primary pulmonary endothelial cells from WT or Ahnak−/− mice in the absence or presence of TGFβ stimulation were performed. We found PCSK9, along with several other candidate genes, was involved in the invasion of melanoma cells into lung tissues. PCSK9 expression in the pulmonary artery was higher in WT mice than Ahnak−/− mice. To evaluate the host function of PCSK9 in lung tissues during the metastasis of melanoma cells, we established lung epithelial cell-specific tamoxifen-induced PCSK9 conditional KO mice (Scgb1a1-Cre/PCSK9fl/fl). The pulmonary metastasis of B16F10 cells in Scgb1a1-Cre/PCSK9fl/fl mice was significantly suppressed, indicating that PCSK9 plays an important role in the metastasis of melanoma cells. Taken together, our data demonstrate that Ahnak regulates metastatic colonization through the regulation of PCSK9 expression.
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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 | 0.001 |
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".