Cancer epigenetics: unraveling etiology and mechanisms to advance prevention
Bibliographic record
Abstract
The increased understanding of epigenetics has significantly advanced our understanding of cancer development, especially regarding environmental, occupational, and lifestyle exposures. Unlike genetic mutations, epigenetic changes may be reversible, making them critical mediators and promising targets for cancer prevention and control. This review synthesizes two decades of transformative research by the International Agency for Research on Cancer (IARC), which positioned the epigenome as a central focus in cancer epidemiology and mechanistic research among the 10 Key Characteristics (KCs) of carcinogens by the IARC Monographs program. From foundational in vitro and animal studies to large-scale population-based research, IARC researchers contributed to unraveling epigenetic mechanisms of carcinogenesis and identifying epigenetic biomarkers of exposures and cancer risk. We highlight progress in epigenetic biomarker development, mechanistic epigenomics, toxico-epigenomics, and the interplay between diet, microbiome, and epigenome. As IARC marks its 60th anniversary, this review underscores the growing role of epigenetics in guiding global cancer prevention efforts and public health strategies.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".