SIRT1 Is Essential for Oncogenic Signaling by Estrogen/Estrogen Receptor α in Breast Cancer
Bibliographic record
Abstract
The NAD-dependent histone deacetylase silent information regulator 1 (SIRT1) is overexpressed and catalytically activated in a number of human cancers, but recent studies have actually suggested that it may function as a tumor suppressor and metastasis inhibitor in vivo. In breast cancer, SIRT1 stabilization has been suggested to contribute to the oncogenic potential of the estrogen receptor α (ERα), but SIRT1 activity has also been associated with ERα deacetylation and inactivation. In this study, we show that SIRT1 is critical for estrogen to promote breast cancer. ERα physically interacted and functionally cooperated with SIRT1 in breast cancer cells. ERα also bound to the promoter for SIRT1 and increased its transcription. SIRT1 expression induced by ERα was sufficient to activate antioxidant and prosurvival genes in breast cancer cells, such as catalase and glutathione peroxidase, and to inactivate tumor suppressor genes such as cyclin G2 (CCNG2) and p53. Moreover, SIRT1 inactivation eliminated estrogen/ERα-induced cell growth and tumor development, triggering apoptosis. Taken together, these results indicated that SIRT1 is required for estrogen-induced breast cancer growth. Our findings imply that the combination of SIRT1 inhibitors and antiestrogen compounds may offer more effective treatment strategies for breast cancer.
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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".