Metabolic Vulnerabilities of Temozolomide-Resistant Glioblastoma Cells: Implications for Targeted Therapies and Overcoming Chemoresistance
Bibliographic record
Abstract
Abstract Chemoresistance is a major clinical challenge in the management of glioblastoma (GB), making it difficult to achieve long-term success with traditional treatments. Therefore, there is a need for the development of novel drugs. We explored the metabolic vulnerabilities of temozolomide (TMZ)-resistant GB and their potential implications for targeted therapies. In monolayer and tumoroid cultures, we found elevated reliance on oxidative phosphorylation in TMZ-resistant cells. Notably, iron reduction in TMZ-resistant cells reduced viability and proliferation, upregulated hypoxia-inducible factor 1-α (Hif1-α) expression, induced autophagy, inhibited autophagic flux, and increased reactive oxygen species (ROS) generation, indicating the significance of iron in metabolic vulnerabilities of these cells. Hypoxic cells showed acquired resistance to iron chelation compared to their normoxic state, suggesting an adaptive mechanism associated to hypoxia. Viability, size, and invasion were reduced in TMZ-resistant tumoroids. Additionally, we reported IC50 for the combination of TMZ with a range of DFO and DFP, making the combination therapy a promising drug candidate to improve therapeutic treatments. Teaser Combining iron reduction and chemotherapy in drug-resistant glioblastoma cells enhances therapeutic outcomes.
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) | 0.000 | 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".