Levels of ANGPTL3 and characterization of other circulating cholesterol-related factors in high-grade serous ovarian cancers.
Bibliographic record
Abstract
e17583 Background: Cancer cells require important amounts of cholesterol to sustain their growth. High-grade serous ovarian carcinomas (HGSOC), as an aggressive form of ovarian cancer, are no exception. Mechanisms allowing HGSOC to secure their lipid supplies are not yet fully elucidated. Recent years have highlighted ANGPTL3, PCSK9 and Apo CIII as key players in lipid metabolism. To date, impact of HGSOC on these key lipid-regulating factors is scantly documented. Herein, we compared ANGPTL3, PCSK9, Apo CIII and Lp(a) levels in women with HGSOC versus benign ovarian lesion (BOL) to better understand lipid metabolism in HGSOC. Methods: Plasma samples from 31 women with a HGSOC and 40 women with a BOL were assayed for ANGPTL3, PCSK9, and Apo CIII levels by ELISA. The lipid panel, Apo B and Lp(a) levels were measured on a Roche Modular analytical platform. Results: Higher levels of ANGPTL3 was observed in HGSOC (84 ng/ml, SD ±29 ng/ml, n = 31) vs. BOL (67 ng/ml, SD ±31 ng/ml, n = 40); HGSOC vs BOL p = 0.019. Receiver operating characteristic (ROC) curve analyses for ANGPTL3 combined with age indicated an area under curve of 67.7% (p = 0.005). Partial correlations indicated associations between the lipid panel and ANGPTL3, but only for BOL. Conclusions: In this cohort of 71 women, ANGPTL3 levels were higher in patients with HGSOC. This finding requires further validation in a larger cohort of HGSOC patients.
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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 | 0.001 |
| 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) | 0.001 | 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".