Cul3 overexpression depletes Nrf2 in breast cancer and is associated with sensitivity to carcinogens, to oxidative stress, and to chemotherapy
Bibliographic record
Abstract
Nrf2 is the key transcription factor for cytoprotective gene programs. Nrf2 is normally maintained at very low concentrations by proteasomal degradation, through its interaction with the adapter protein Keap1 and the Cul3 E3 ligase. Increased Nrf2 concentration resulting from loss of function Keap1 mutations has been described in chemoresistant non-small cell lung cancer. Previous studies in breast cancer showed low levels of some Nrf2-regulated detoxification genes, but the mechanism has not been systematically examined. We found that half of the breast cancer cell lines examined have decreased concentration of Nrf2 compared with normal mammary epithelial cell lines, associated with variable but detectable levels in Keap1 levels, and consistently increased Cul3 mRNA and protein. Immunochemistry showed that 7 of 10 breast cancer specimens examined also have low Nrf2 levels and increased Cul3. Keap1 protein levels are variable. We found no C23Y mutation in Keap1 of any of the cell lines. Using siRNA, we silenced Cul3 in MCF-7 breast cancer cells, and microarray analysis reveals the induction of GCL, NQO1, AKR1C1, UGDH, and TXN by at least 2-fold. The Nrf2-regulated ABCC1 drug transporter was also found to be increased. These Cul3-silenced MCF7 cells are highly resistant to oxidative stress induced by H(2)O(2,) to the carcinogen benzo(a)pyrene, and to both Doxorubicin and Paclitaxel. This high Cul3/low Nrf2 signature may be key to cellular sensitivity to both chemical carcinogeneic stimuli as well as to cytotoxicity of commonly used chemotherapeutic drugs in established breast cancers.
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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".