Proprotein Convertase Subtilisin/Kexin Type 9 Deficiency Reduces Melanoma Metastasis in Liver
Bibliographic record
Abstract
High circulating cholesterol is associated with hypercholesterolemia, atherosclerosis, and stroke. However, the relation between cholesterol and tumorigenesis/metastasis is controversial. The proprotein convertase subtilisin/kexin type 9 (PCSK9) regulates low-density lipoprotein cholesterol homeostasis by targeting the low-density lipoprotein receptor (LDLR) for degradation. PCSK9 is mostly expressed in liver, which is one of the most common sites for metastatic disease. To reveal the function of PCSK9 and also evaluate the impact of cholesterol in liver metastasis development, B16F1 melanoma cells were injected into wild-type (WT) and Pcsk9(-/-) mice to induce liver metastasis. On chow diet, Pcsk9(-/-) mice harbored two-fold less liver metastases than WT mice. This decrease is related to low cholesterol levels in Pcsk9(-/-) mice, as the protection was lost after normalizing Pcsk9(-/-) cholesterol levels by a 2-week high cholesterol diet. Furthermore, a prolongation of this diet strongly increased metastasis in both genotypes, suggesting that high cholesterol levels promote metastatic progression. The protective effect of the PCSK9 deficiency is also associated with increased apoptosis in liver stroma and metastases. Tumor necrosis factor.α (TNFα) mRNA and protein were, respectively, higher in liver stroma and plasma of injected mice, likely increasing the apoptotic TNFα signaling. Furthermore, the anti-apoptotic factor B-cell lymphoma 2 was downregulated. TNFα regulation is LDLR-independent, as its mRNA level was similarly upregulated in mice lacking both PCSK9 and LDLR. Our findings show that PCSK9 deficiency reduces liver metastasis by its ability to lower cholesterol levels and by possibly enhancing TNFα-mediated apoptosis.
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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 | 0.000 |
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".