Pan-cancer landscape of aberrant DNA Methylation across childhood Cancers: Molecular Characteristics and Clinical relevance
Bibliographic record
Abstract
Aberrant DNA methylation (DNAm) is an important epigenetic regulator in various cancers. Pan-cancer DNAm analyses have investigated the potential common mechanisms of DNAm in tumorigenesis. However, these pan-cancer studies focused on adult cancers rather than pediatric cancers, which may have distinct pathology and treatment responses. Here, we performed a pan-cancer analysis of genome-wide DNAm in over 2,000 samples from nine pediatric cancers to elucidate the DNAm landscape of pediatric cancers. We identified 217,586 differentially methylated CpG sites (DMCs) in pediatric cancers, with a tendency toward hypermethylation as opposed to hypomethylation (P = 0.02). Amongst them, 75.65% also presented DNAm alterations in adult cancers. In nine pediatric cancers, we defined 54 shared DMCs (SDMCs), which were also observed in at least one adult cancer type. Furthermore, methylation patterns in SDMCs influenced the transcription of several genes (MEIS1, MIA3, PCDHAC2, SH3BP4, and ATP8B1) involved in well-known cancer-related pathways and cancer hallmarks (FDR < 0.05). Moreover, SDMCs were significantly associated with patient survival, and this association was independent of sex, age, and tumor stage (P < 0.05). Interestingly, SDMCs could affect patient survival not only in the nine pediatric cancers that were used to identify SDMCs but also in other untested pediatric cancers (P < 0.05). Collectively, our data depicts a comprehensive landscape of aberrant DNA methylation in pediatric cancers, which is partly similar to that of adult cancers. We also suggest a potential clinical application of SDMCs as biomarkers for the prognosis of pediatric cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".