NRF1 or NRF2: Emerging Role of Redox Homeostasis on PERK/NRF/Autophagy Mediated Antioxidant in Tumor and Patient Dependent Chemo Sensitivity
Bibliographic record
Abstract
Chemo-resistance is a substantial challenge in the realm of cancer treatment that requires exploring new therapeutic approaches for effective mitigation. Achieving this goal requires examination of the molecular mechanisms involved in both tumor growth and therapeutic interventions. The potential of NRF2 (Nuclear factor E2-related factor 2) in addressing resistance to chemotherapy across diverse cancer types highlights its value as a promising therapeutic approach based on cancer characteristics. Manipulating the NRF2 signaling pathway has a dual impact, offering promise for both preventing and treating cancer, as well as inhibiting carcinogenesis. The influence of the NRF2/KEAP1 pathway on the progression of tumor formation and resistance to drugs has been well-documented. The interplay between the NRF2 signaling pathway and processes such as endoplasmic reticulum (ER) stress, unfolded protein response (UPR), and autophagy plays a crucial protective role. A deeper understanding of NRF2's role in the modulating these pathways is necessary to develop novel approaches for improving chemotherapeutic efficacy. This article discusses the significance of the NRF2-KEAP1 pathway in preventing/promoting cancer and resistance mechanisms to various chemotherapeutic agents, with a focus on the complementary effects of antioxidants via NRF2-mediated signaling pathways. This study aims to provide a molecular basis for targeting NRF2 via inhibitors/activators as promising therapeutic strategies to overcome chemo-resistance.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".