Flavonoids Regulate Redox-Responsive Transcription Factors in Glioblastoma and Microglia
Bibliographic record
Abstract
The tumor microenvironment (TME) has emerged as a valuable therapeutic target in glioblastoma (GBM), as it promotes tumorigenesis via an increased production of reactive oxygen species (ROS). Immune cells such as microglia accumulate near the tumor and its hypoxic core, fostering tumor proliferation and angiogenesis. In this study, we explored the therapeutic potential of natural polyphenols with antioxidant and anti-inflammatory properties. Notably, flavonoids, including fisetin and quercetin, can protect non-cancerous cells while eliminating transformed cells (2D cultures and 3D tumoroids). We tested the hypothesis that fisetin and quercetin are modulators of redox-responsive transcription factors, for which subcellular location plays a critical role. To investigate the sites of interaction between natural compounds and stress-responsive transcription factors, we combined molecular docking with experimental methods employing proximity ligation assays. Our findings reveal that fisetin decreased cytosolic acetylated high mobility group box 1 (acHMGB1) and increased transcription factor EB (TFEB) abundance in microglia but not in GBM. Moreover, our results suggest that the most powerful modulator of the Nrf2-KEAP1 complex is fisetin. This finding is in line with molecular modeling and calculated binding properties between fisetin and Nrf2-KEAP1, which indicated more sites of interactions and stronger binding affinities than quercetin.
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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) | 0.001 | 0.000 |
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".