17β-estradiol-induced ACSL4 protein expression promotes an invasive phenotype in estrogen receptor positive mammary carcinoma cells
Bibliographic record
Abstract
Long chain acyl-CoA synthase-4 (ACSL4) expression has been associated with an aggressive phenotype in breast carcinoma cells, whereas its role in ERα-positive breast cancer has not been studied. ACSL4 prefers 20-carbon polyunsaturated fatty acid (PUFA) substrates, and along with other ACSLs has been associated with cellular uptake of exogenous fatty acids. 17β-estradiol induces proliferation and invasive capacities in ERα+ve breast carcinoma that is associated with modifications of cellular lipid metabolism. In this study, treatment of steroid-starved ERα-positive MCF-7 and T47D mammary carcinoma cells with 17β-estradiol resulted in increased cellular uptake of the PUFA arachidonic acid (AA) and eicosapentaenoic acid (EPA), important building blocks for cellular membranes, and increased ACSL4 protein levels. There was no change in the expression of the ACSL1, ACSL3 and ACSL6 protein isotypes. Increased ACSL4 protein expression was not accompanied by changes in ACSL4 mRNA expression, but was associated with a significant increase in the protein half-life compared to untreated cells. ERα silencing reversed the impact of 17β-estradiol on ACSL4 protein levels and half-life. Silencing of ACSL4 eliminated the 17β-estradiol-induced increase in AA and EPA uptake, as well as the 17β-estradiol-induced cell migration, proliferation and invasion capacities. ASCL4 silencing also prevented the 17β-estradiol induced increases in p-Akt and p-GSK3β, and decrease in E-cadherin expression, important events in epithelial to mesenchymal transition. Taken together, these results demonstrate that ACSL4 is a target of 17β-estradiol-stimulated ERα and is required for the cellular uptake of exogenous PUFA and the manifestation of a more malignant phenotype in ERα+ve breast carcinoma cells.
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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".