Microarray Analysis Reveals Glucocorticoid-Regulated Survival Genes That Are Associated With Inhibition of Apoptosis in Breast Epithelial Cells
Bibliographic record
Abstract
Activation of the glucocorticoid receptor (GR) results in diverse physiological effects depending on cell type. For example, glucocorticoids (GC) cause apoptosis in lymphocytes but can rescue mammary epithelial cells from growth factor withdrawal-induced death. However, the molecular mechanisms of GR-mediated survival remain poorly understood. In this study, a large-scale oligonucleotide screen of GR-regulated genes was performed. Several of the genes that were found to be induced 30 min after GR activation encode proteins that function in cell survival signaling pathways. We also demonstrate that dexamethasone pretreatment of breast cancer cell lines inhibits chemotherapy-induced apoptosis in a GR-dependent manner and is associated with the transcriptional induction of at least two genes identified in our screen, serum and GC-inducible protein kinase-1 (SGK-1) and mitogen-activated protein kinase phosphatase-1 (MKP-1). Furthermore, GC treatment alone or GC treatment followed by chemotherapy increases both SGK-1 and MKP-1 steady-state protein levels. In the absence of GC treatment, ectopic expression of SGK-1 or MKP-1 inhibits chemotherapy-induced apoptosis, suggesting a possible role for these proteins in GR-mediated survival. Moreover, specific inhibition of SGK-1 or MKP-1 induction by the introduction of SGK-1- or MKP-1-small interfering RNA reversed the anti-apoptotic effects of GC treatment. Taken together, these data suggest that GR activation in breast cancer cells regulates survival signaling through direct transactivation of genes that encode proteins that decrease susceptibility to apoptosis. Given the widespread clinical administration of dexamethasone before chemotherapy, understanding GR-induced survival mechanisms is essential for achieving optimal therapeutic responses.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".