Bibliographic record
Abstract
The Kelch-like ECH-associated protein 1/nuclear factor erythroid-derived 2-like 2 (KEAP1-NRF2) system is a pivotal defense mechanism against oxidative and electrophilic stress. Although transient NRF2 activation in response to stress is beneficial for health, persistent NRF2 activation in cancer cells has deleterious effects on cancer-bearing hosts by conferring therapeutic resistance and aggressive tumorigenic activity on cancer cells. Because NRF2 increases the antioxidant and detoxification capability of cancer cells, persistently high levels of NRF2 activity enhance therapeutic resistance of cancer cells. NRF2 also drives metabolic reprogramming to establish cellular metabolic processes that are advantageous for cell proliferation in cooperation with other oncogenic pathways. As a result of these advantages, cancer cells with persistent activation of NRF2 often develop "NRF2 addiction" and show malignant phenotypes leading to poor prognoses in cancer patients. Inhibition of NRF2 is a promising therapeutic approach for NRF2-addicted cancers and NRF2 inhibitors are being actively developed. However, giving systemic NRF2 inhibitors might have undesirable effects on cancer-bearing hosts, considering the central roles of NRF2 in cytoprotection. To avoid these side-effects, new therapeutic targets besides NRF2 for NRF2-addicted cancers have been actively explored. This review introduces recent studies describing the development and characterization of NRF2-addicted cancers, as well as their potential therapeutic targets. Expected advances in diagnostic and therapeutic interventions for NRF2-addicted cancers are also discussed.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".