Role of MacroH2A2 in the glioblastoma stem cell epigenome
Bibliographic record
Abstract
Glioblastoma is the most common primary malignant brain tumour in adults, and remains uniformly lethal. These tumours contain a subpopulation of glioblastoma stem cells (GSCs) that drive tumour recurrence and drug resistance. We find that MacroH2A2 is a histone variant that can stratify glioblastoma patients, with higher levels of this histone variant associated with better patient prognosis. Knockdown of macroH2A2 in GSCs is associated with increased self-renewal and an increased expression of stemness genes by RNA-seq. Our preliminary results suggest that macroH2A2 is a novel biomarker for glioblastoma and that macroH2A2 loss is a marker of GSC stemness and a poor prognostic marker in glioblastoma. This work identifies loss of macroH2A2 as a feature of GSCs and provides a framework for therapeutic modulation of this histone variant. LEARNING OBJECTIVES This presentation will enable the learner to: 1. Explain the role of epigenetics in glioblastoma pathophysiology
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".