Nuclear Factor-κB Induced by Doxorubicin Is Deficient in Phosphorylation and Acetylation and Represses Nuclear Factor-κB–Dependent Transcription in Cancer Cells
Bibliographic record
Abstract
The primary goal of chemotherapy is to cause cancer cell death. However, a side effect of many commonly used chemotherapeutic drugs is the activation of nuclear factor-kappaB (NF-kappaB), a potent inducer of antiapoptotic genes, which may blunt the therapeutic efficacy of these compounds. We have assessed the effect of doxorubicin, an anthracycline in widespread clinical use, on NF-kappaB activation and expression of antiapoptotic genes in breast cancer cells. We show that doxorubicin treatment activates NF-kappaB signaling and produces NF-kappaB complexes that are competent for NF-kappaB binding in vitro. Surprisingly, these NF-kappaB complexes suppress, rather than activate, constitutive- and cytokine-induced NF-kappaB-dependent transcription. We show that doxorubicin treatment produces RelA, which is deficient in phosphorylation and acetylation and which blocks NF-kappaB signaling in a histone deacetylase-independent manner, and we show that NF-kappaB activated by doxorubicin does not remain stably bound to kappaB elements in vivo. Together these data show that NF-kappaB signaling induced by doxorubicin reduces expression of NF-kappaB-dependent genes in 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 | 0.001 |
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".