DNA hypohydroxymethylation in pediatric central nervous system tumors is associated with CTCF binding sites and reduced survival
Bibliographic record
Abstract
Abstract Nucleotide-specific 5-hydroxymethylcytosine (5hmC) remains understudied in pediatric central nervous system tumors. We measured genome-scale 5hmC in glioma, ependymoma, and embryonal tumors from children, as well as control pediatric brain tissues using oxidative and bisulfite treatments. Tumor 5hmC localized to regulatory elements crucial to cell identity, including transcription factor binding sites and super-enhancers. A linear model tested the CpG-specific differences in 5hmC between tumor and non-tumor samples, as well as between tumor subtypes. Compared to non-tumor samples, tumors were hypohydroxymethylated across the epigenome. Differentially hydroxymethylated loci among tumor subtypes tended to be hypermethylated and disproportionally found in CTCF binding sites and genes related to posttranscriptional RNA regulation, such as DICER1 . Model-based clustering results indicated that patients with low 5hmC patterns have poorer overall survival and increased risk of recurrence. These results have implications for emerging molecular neuropathology classification approaches and epigenetic therapeutic strategies in childhood brain tumors.
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".