Raloxifene prevents stress granule dissolution, impairs translational control and promotes cell death during hypoxia in glioblastoma cells
Bibliographic record
Abstract
Glioblastoma (GBM) is the most common primary malignant brain tumor, and it has a uniformly poor prognosis. Hypoxia is a feature of the GBM microenvironment, and previous work has shown that cancer cells residing in hypoxic regions resist treatment. Hypoxia can trigger the formation of stress granules (SGs), sites of mRNA triage that promote cell survival. A screen of 1120 FDA-approved drugs identified 129 candidates that delayed the dissolution of hypoxia-induced SGs following a return to normoxia. Amongst these candidates, the selective estrogen receptor modulator (SERM) raloxifene delayed SG dissolution in a dose-dependent manner. SG dissolution typically occurs by 15 min post-hypoxia, however pre-treatment of immortalized U251 and U3024 primary GBM cells with raloxifene prevented SG dissolution for up to 2 h. During this raloxifene-induced delay in SG dissolution, translational silencing was sustained, eIF2α remained phosphorylated and mTOR remained inactive. Despite its well-described role as a SERM, raloxifene-mediated delay in SG dissolution was unaffected by co-administration of β-estradiol, nor did β-estradiol alone have any effect on SGs. Importantly, the combination of raloxifene and hypoxia resulted in increased numbers of late apoptotic/necrotic cells. Raloxifene and hypoxia also demonstrated a block in late autophagy similar to the known autophagy inhibitor chloroquine (CQ). Genetic disruption of the SG-nucleating proteins G3BP1 and G3BP2 revealed that G3BP1 is required to sustain the raloxifene-mediated delay in SG dissolution. Together, these findings indicate that modulating the stress response can be used to exploit the hypoxic niche of GBM tumors, causing cell death by disrupting pro-survival stress responses and control of protein synthesis.
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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".