Estrogens desensitize MCF-7 breast cancer cells to apelin-induced autophagy and enhanced growth under estrogen starvation: a possible implication in endocrine resistance
Bibliographic record
Abstract
Apelin-13 is an adipokine known for its growth-inducing effects on human breast cancer cells in an estrogen-containing environment. However, the response of these cells to apelin-13 in the absence of estrogen and its association with the expression of the apelin receptor (APLNR) has not yet been investigated. In the present study, we show that the breast cancer cell line MCF-7 expresses the APLNR as shown by immunofluorescence and flow cytometry, under conditions of ER starvation and that culture of these cells in the presence of apelin-13 results in an increased growth rate and a diminished autophagy flux. Moreover, the binding of APLNR by apelin-13 resulted in an increased growth rate (assayed by AlamarBlue) and a diminished autophagy flux (monitored by Lysotracker Green). The latter observations were reversed in the presence of exogenous estrogen. Finally, apelin-13 induces the deactivation of the apoptotic kinase AMPK. Taken together, our results show that APLNR signaling in breast cancer cells is functional and prevents tumor growth under conditions of estrogen starvation. They furthermore suggest an alternative mechanism of estrogen-independent tumor growth thereby positioning the APLNR-AMPK axis as a novel pathway and a possible therapeutic target in endocrine resistance of breast cancer 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 | 0.000 |
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".