DNA methylation at enhancer regions Novel avenues for epigenetic biomarker development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Biomarkers are molecules or features which can provide clinically-relevant information about a particular disease state, thus providing useful tools for oncologists. Recently, a number of studies have demonstrated that DNA methylation holds great promise as a novel source of cancer biomarkers. Although promoter regions have been the focus of most investigations thus far, mounting evidence demonstrates that enhancer sequences also undergo extensive differential methylation in cancer cells. Moreover, enhancer methylation correlates with target gene expression better than promoter methylation, providing unexplored strategies for biomarker development. Here, we review important considerations associated with the clinical analysis of DNA methylation at distal regulatory regions. Notably, we highlight emerging literature addressing the methylation status of enhancers in development and cancer, and subsequently discuss how enhancer methylation can be exploited to guide disease management. While acknowledging current limitations, we propose that the methylation state of enhancer regions has the potential to headline the next generation of epigenetic biomarkers.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| 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 it