Histone H3K4/H3K9 Methylation Dynamics Gate Cell Fate Plasticity In Pediatric High-Grade Gliomas: A Systems-Level Attractor Reconstruction
Bibliographic record
Abstract
Background: This study employs computationally driven systems medicine approaches, integrating complexity theory and machine learning-driven discovery, to identify key biomarkers governing phenotypic plasticity in pediatric high-grade gliomas (pHGGs), specifically IDH-wildtype (IDHWT) glioblastoma and H3K27M Diffuse Midline Glioma (DMG). Methods: We integrate single-cell transcriptomics and histone mass cytometry to conceptualize pHGGs as complex adaptive ecosystems. Through attractor landscape reconstruction, network inference and algorithmic complexity measures, we computationally infer epigenetic and transcriptional regulators that steer cell fate cybernetics. Results: Our findings predict lineage-specific plasticity signatures, including KDM5B (JARID1B), ARID5B, GATA2/6, DLX5/6, FOXA2/FOXO1/FOXO3, ASCL4, ATF3, PRDM9, METTL5/8, RAP1B, DKK3, DOCK7, RLIM, TERF1, CD99, FABP5/7 and LAPTM5, alongside WNT, TGF[Formula: see text] and NOTCH pathway regulators, with subtype-specific and shared patterns across K27M and IDHWT tumors. We also identify endogenous bioelectric markers (GRIK3, GRIN3B, SLC5A9, NKAIN4, KCNJ4/6) as potential reprogramming targets. Previously reported plasticity signatures (PDGFRA, EGFR targets, OLIG1/2, FXYD5/6, MTSS1, SEZ6L, MTRNR2L1, SOX11) were independently recovered, supporting the robustness of our computational predictions. Collectively, results suggest that pHGG cell fates are biased toward neuronal-like identities, reflecting a teleonomic endpoint along pathological developmental attractors. Conclusions: By combining complex-systems modeling with translational AI, we predict in silico plasticity biomarkers and therapeutic targets for preventive, predictive and precision care. This work is computational-only and awaits experimental validation.
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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 | 0.001 |
| 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".