Circulating levels of PCSK9, ANGPTL3, and other lipid-related factors in men with high-grade prostate cancer.
Bibliographic record
Abstract
e17001 Background: Cancer cells rely on abundant lipid supplies for growth and metastasis. Previous studies have shown that lipid-lowering drugs provide a protective effect against cancer progression, including prostate cancer. However, the role of hyperlipidemic factors has not yet been extensively studied. Here, we aimed to measure circulating levels of hyperlipidemic factors, such as PCSK9 and ANGPTL3, in a small cohort of men diagnosed with high-grade prostate cancer compared to men without cancer. Methods: Plasma samples and data were obtained from 70 consenting men treated at CHU de Québec-Université Laval. Thirty-five patients with high-grade prostate cancer (cases) were age and BMI-matched to 35 patients without cancer (controls). The circulating levels of various lipids, Apo B and Lp(a) were obtained with a Roche Modular analytical platform, whereas levels of PCSK9, ANGPTL3 and leptin were measured by ELISA. We performed group comparisons and correlations using R version 4.2.1. Results: As expected, Prostate-specific antigen (PSA) levels were significantly higher in the cancer group. The lipid profile as well as Lp(a) and leptin levels did not differ between groups. ANGPTL3 level tended to be lower in patients with prostate cancer without reaching statistical significance (mean 32.2 vs 36.3 ng/mL, p = 0.051). ANGPTL3 level also inversely correlated with leptin among the cases (rho = -0.44, p = 0.008). PCSK9 level was similar in both groups, but inversely correlated with PSA in the cancer group (rho = -0.39, p = 0.022). PSA level correlated with triglycerides, total cholesterol, Apo B, non-HDL-cholesterol and LDL in the control group (p < 0.05). Conclusions: None of the hyperlipidemic factors measured here was significantly different between high-grade prostate cancer patients and controls. However, the trend for lower ANGPTL3 levels in cancer, as well as the inverse correlations between PCSK9 and PSA and between ANGPTL3 and leptin levels in cancer patients, warrant further investigation in larger cohorts.
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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 | 0.001 | 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) | 0.002 | 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".