ID1 Is Critical for Tumorigenesis and Regulates Chemoresistance in Glioblastoma
Bibliographic record
Abstract
Glioblastoma is the most common primary brain tumor in adults. While the introduction of temozolomide chemotherapy has increased long-term survivorship, treatment failure and rapid tumor recurrence remains universal. The transcriptional regulatory protein, inhibitor of DNA-binding-1 (ID1), is a key regulator of cell phenotype in cancer. We show that CRISPR-mediated knockout of ID1 in glioblastoma cells, breast adenocarcinoma cells, and melanoma cells dramatically reduced tumor progression in all three cancer systems through transcriptional downregulation of EGF, which resulted in decreased EGFR phosphorylation. Moreover, ID1-positive cells were enriched by chemotherapy and drove tumor recurrence in glioblastoma. Addition of the neuroleptic drug pimozide to inhibit ID1 expression enhanced the cytotoxic effects of temozolomide therapy on glioma cells and significantly prolonged time to tumor recurrence. Conclusively, these data suggest ID1 could be a promising therapeutic target in patients with glioblastoma. SIGNIFICANCE: These findings show that the transcriptional regulator ID1 is critical for glioblastoma initiation and chemoresistance and that inhibition of ID1 enhances the effect of temozolomide, delays tumor recurrence, and prolongs survival.
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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 | 0.001 |
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".