Lipoprotein Lipase Hydrolysis Products Induce Pro-Inflammatory Cytokine Expression in Triple-Negative Breast Cancer Cells
Bibliographic record
Abstract
Abstract Objectives: Lipoprotein lipase (LPL) is an extracellular lipase that hydrolyzes triacylglycerols and phospholipids from lipoproteins. LPL is highly expressed in adipose tissue and expressed in some breast cancer cell lines. Hydrolysis products generated by LPL can be used by cells as components of the cell membrane, as an energy supply, or as signaling molecules. Therefore, LPL on or around cancer cells may contribute to breast cancer growth and progression. We hypothesized that hydrolysis products generated by LPL from total lipoproteins can promote pro-inflammatory cytokine secretion from breast cancer cells and/or affect viability. Results: Using cytokine arrays, we found that the secretion of seven cytokines was increased by MDA-MB-231 cells treated with lipoprotein hydrolysis products. An increased secretion of TNF-α and IL-6 was also seen by MDA-MB-468 cells, and an increase in IL-4 secretion was seen by MDA-MB-468 and SKBR3 cells. In contrast, MCF-7 cells showed a decreased secretion of only two cytokines. The changes to cytokine secretion profiles by the breast cancer cell types, including by non-cancerous MCF-10a breast cells, were independent of increased cell metabolic activity. Overall, these results provide information on how lipoprotein hydrolysis products within the tumor microenvironment might affect breast cancer cell viability and tumor 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 | 0.000 |
| 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".