Eukaryotic initiation factor 5B (eIF5B) regulates temozolomide-mediated apoptosis in brain tumour stem cells (BTSCs)
Bibliographic record
Abstract
Glioblastoma multiforme (GBM) is among the deadliest cancers, owing in part to complex inter- and intra-tumor heterogeneity and the presence of a population of stem-like cells called brain tumour stem cells (BTSCs/BTICs). These cancer stem cells survive treatment and confer resistance to the current therapies - namely, radiation and the chemotherapeutic, temozolomide (TMZ). TMZ induces cell death by alkylating DNA, and BTSCs resist this mechanism via a robust DNA damage response. Hence, recent studies aimed to sensitize BTSCs to TMZ using combination therapy, such as inhibition of DNA repair machinery. We have previously demonstrated in established GBM cell lines that eukaryotic initiation factor 5B (eIF5B) promotes the translation of pro-survival and anti-apoptotic proteins. Consequently, silencing eIF5B sensitizes these cells to TRAIL-induced apoptosis. However, established cell lines do not always recapitulate the features of human glioma. Therefore, we investigated this mechanism in patient-derived BTSCs. We show that silencing eIF5B leads to increased TMZ sensitivity in two BTSC lines: BT25 and BT48. Depletion of eIF5B decreases the levels of anti-apoptotic proteins in BT48 and sensitizes these cells to TMZ-induced activation of caspase-3, cleavage of PARP, and apoptosis. We suggest that eIF5B represents a rational target to sensitize GBM tumors to the current standard-of-care.
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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".