A Mutant High-Density Lipoprotein Receptor Inhibits Proliferation of Human Breast Cancer Cells
Bibliographic record
Abstract
High-density lipoprotein (HDL) stimulates the growth of many types of cells, including those of breast cancer. High levels of HDL are associated with an increased risk of breast cancer development. A scavenger receptor of the B class (SR-BI)/human homolog of SR-BI, CD36, and LIMPII analogous-1 (CLA-1) facilitates the cellular uptake of cholesterol from HDL and thus augments cell growth. Furthermore, HDL is also believed to have antiapoptotic effects on various cell types, and this feature adds to its ability to promote cell growth. These collaborative roles of HDL and CLA-1 prompted us to assess the function of these components on human breast cancer cells. In this study, we created a mutant CLA-1 (mCLA) that lacked the COOH-terminal tail to determine its potential role in breast cancer cell growth. Expression of mCLA inhibited the proliferation of breast cancer cell line MCF-7. This inhibitory action of mCLA required the transcriptional factor activator protein-1 (AP-1), and the mutant receptor also affected the antiapoptotic features of HDL. The effect of HDL on AP-1 activation and [(3)H]thymidine incorporation was abrogated by wortmannin, a specific inhibitor of phosphoinositide 3-kinase. Furthermore, the dominant negative mutant of Akt abolished the ability of HDL to activate AP-1. These findings raise the possibility that the inhibitors of the effects of HDL may be of therapeutic value for breast cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".