E2F and STAT3 provide transcriptional synergy for histone variant H2AZ activation to sustain glioblastoma chromatin accessibility and tumorigenicity
Bibliographic record
Abstract
The histone variant H2AZ is overexpressed in diverse cancer types where it facilitates the accessibility of transcriptional regulators to the promoters of cell cycle genes. However, the molecular basis for its dysregulation in cancer remains unknown. Here, we report that glioblastomas (GBM) and glioma stem cells (GSCs) preferentially overexpress H2AZ for their proliferation, stemness and tumorigenicity. Chromatin accessibility analysis of H2AZ2 depleted GSC revealed that E2F1 occupies the enhancer region within H2AZ2 gene promoter, thereby activating H2AZ2 transcription. Exploration of other H2AZ2 transcriptional activators using a customized "anti-H2AZ2" query signature for connectivity map analysis identified STAT3. Co-targeting E2F and STAT3 synergistically reduced the levels of H2AZ, histone 3 lysine 27 acetylation (H3K27ac) and cell cycle gene transcription, indicating that E2F1 and STAT3 synergize to activate H2AZ gene transcription in GSCs. Remarkably, an E2F/STAT3 inhibitor combination durably suppresses GSC tumorigenicity in an orthotopic GBM xenograft model. In glioma patients, high STAT3 signaling is associated with high E2F1 and H2AZ2 expression. Thus, GBM has uniquely opted the use of E2F1- and STAT3-containing "enhanceosomes" that integrate multiple signaling pathways to achieve H2AZ gene activation, supporting a translational path for the E2F/STAT3 inhibitor combination to be applied in GBM treatment.
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) | 0.002 | 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".