Epigenetic biomarkers in colorectal cancer diagnostics
Bibliographic record
Abstract
Colorectal cancer (CRC) is a significant health burden worldwide. Despite advancements in treatment options, improvements in CRC patient survival have been limited owing to lack of early detection and limited capacity for optimal therapeutic decision-making. Biomarkers to improve CRC diagnosis, prognosis and prediction of treatment response therefore represent opportunities to improve patient outcome. In addition to genetic alterations and genomic instability, it is now clear that epigenetic alterations play dramatic roles in driving tumor onset and progression in CRC. A recent surge in investigation of epigenetic biomarkers including DNA methylation, miRNA expression and histone modifications has demonstrated that these alterations may be enticing translational biomarker candidates in CRC. In particular, methylation kits have already been incorporated into clinical practice for a handful of cancers, including CRC. This review will aim to summarize the established and emerging roles of epigenetic modifications in CRC detection, prognostication and prediction of treatment response.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".