Genetic, metabolic and immunological features of cancers with <scp>NRF2</scp> addiction
Bibliographic record
Abstract
Nuclear factor erythroid-derived 2-like 2 (NRF2) is a master transcription factor that coordinately regulates the expression of many cytoprotective genes and plays a central role in defense mechanisms against oxidative and electrophilic insults. Although increased NRF2 activity is principally beneficial for our health, NRF2 activation in cancer cells is detrimental. Many human cancers exhibit persistent NRF2 activation and such cancer cells rely on NRF2 for most of their malignant characteristics, such as therapeutic resistance and aggressive tumourigenesis, and thus fall into NRF2 addiction. The persistent activation of NRF2 confers great advantages on cancer cells, whereas it is not tolerated by normal cells, suggesting that certain requirements are necessary for a cell to exploit NRF2 and evolve into malignant cancer cells. In this review, recent reports and data on the genetic, metabolic and immunological features of NRF2-activated cancer cells are summarized, and prerequisites for NRF2 addiction in cancer cells and their therapeutic applications are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".