Lipoprotein lipase hydrolysis products induce pro-inflammatory cytokine expression in triple-negative breast cancer cells
Bibliographic record
Abstract
OBJECTIVES: Breast cancer cell growth and proliferation requires lipids for energy production, cell membrane synthesis, or as signaling molecules. Lipids can be delivered to cells by lipoprotein lipase (LPL), an extracellular lipase that hydrolyzes triacylglycerols and phospholipids from lipoproteins, that is expressed by adipose tissue and some breast cancer cell lines. Studies have shown that lipoprotein hydrolysis products induce pro-inflammatory cytokine secretion by endothelial cells. Thus, our objective was to determine if hydrolysis products generated by LPL from total lipoproteins can also promote pro-inflammatory cytokine secretion from breast cancer cells. RESULTS: Using cytokine arrays, we found that MDA-MB-231 cells increased secretion of seven cytokines in response to treatment with lipoprotein hydrolysis products. In contrast, MCF-7 cells showed decreased secretion of two cytokines. Expanding the analysis to additional cell lines by ELISA, we found increased secretion of TNF-α and IL-6 by MDA-MB-468 cells, and increased secretion of IL-4 by MDA-MB-468 and SKBR3 cells. The changes to cytokine secretion profiles of the breast cancer cell types examined, including the non-cancerous MCF-10a breast cells, were independent of increased cell metabolic activity. These results provide information on how lipoprotein hydrolysis products within the tumor microenvironment might affect breast cancer cell viability and progression.
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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".