STEM-09. EXPLORING THE ROLE OF <i>TP53</i> IN REDOX REGULATION AND RESPONSE OF GLIOBLASTOMA STEM CELLS TO ROS-INDUCING AGENTS
Bibliographic record
Abstract
Abstract Glioblastoma, the most malignant primary tumor of the central nervous system in adults presents a major therapeutic challenge due to the presence of chemo-radioresistant glioblastoma stem cells (GSCs). These GSCs adapt the thioredoxin (Trx) and glutathione (GSH) antioxidant systems to counteract treatment-induced reactive oxygen species (ROS) and promote their survival. The tumor suppressor gene wild-type (wt)TP53 regulates the expression of pro-oxidant or antioxidant genes in response to chemoradiotherapy based on p53 DNA-binding activity in a reducing microenvironment. Given the importance of p53 as a redox-sensitive protein, we hypothesized that p53 might protect GSCs against ROS-inducing agents. Auranofin (Au), a FDA-approved gold(I)-containing compound inhibits thioredoxin reductase 1 (TrxR1), a key antioxidant protein. We investigated the effects of Au on different GSCs including wtp53 GSC line and its counterpart p53-knockdown. We assessed viability, neurosphere formation, ROS levels and expression of p53 and TrXR1 to evaluate the cytotoxicity of Au, alone and in combination with L-buthionine sulfoximine (L-BSO), a GSH-depleting agent. We conducted TCGA analysis to explore the correlation between TrxR1 and p53. Our findings revealed a positive significant correlation between p53 and TrxR1 expression, consistent with lower levels of TrxR1 in p53-knockdown GSCs compared to their wt-p53 counterparts. P53-knockdown GSCs displayed a higher basal level of ROS, which further increased upon treatment with Au. P53-knockdown significantly increased the sensitivity of cells to Au, with pronounced effects for Au/L-BSO combination. Our findings reveal that expression levels of wtp53 affect the role of p53 in TrxR1-redox regulation and sensitivity to ROS-inducing agents in GSCs.
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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) | 0.003 | 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".