Epigenetic aging of colorectal mucosa in cancer development
Menée à l'aide d'échantillons de tissus coliques normaux, de tissus tumoraux et de tissus adjacents d'origine humaine, cette étude examine l'âge épigénétique de ces différents tissus en fonction des modèles utilisés, des modes de vie et de la région colique considérée
Background : The past decade has seen the development of epigenetic models of aging that accurately estimate chronological age and predict disease incidence and mortality. These estimates are modulated by lifestyle and environmental factors linked to carcinogenesis, but to date this has primarily been studied in blood.
Methods : We examined epigenetic aging in normal colonic tissue (n = 96), adjacent mucosa (n = 245) and tumors (n = 208), using models trained on age (Horvath, Hannum, Zhang), mortality (PhenoAge, GrimAge), aging rate (DunedinPACE), cellular mitotic history (EpiTOC, epiTOC2, miAGe), and telomere length (DNAmTL).
Results : The Horvath model was the most accurate estimator of chronological age in normal colonic mucosa, with high correlation (r > 0.70) between the Horvath, Hannum, Zhang, PhenoAge and GrimAge models, and between mitotic clocks (r > 0.94). All models showed similar performance in normal tissue and adjacent mucosa, but substantially more variation in estimates in tumors. Significant differences in age acceleration were present between normal and adjacent mucosa by six models (Hannum, Zhang, PhenoAge, EpiTOC, epiTOC2 and miAge), while tumors showed highly significant differences by all models. Age acceleration differed by region of the colon, with varying patterns by model type. Physical activity (PhenoAge), smoking history (GrimAge), and alcohol consumption (Horvath, mitotic clocks) were associated with epigenetic aging in adjacent mucosa, while smoking history, smoking intensity, and alcohol consumption were associated with DNAmTL in tumors.
Conclusions : Our study reveals an impact of tissue type, region, and lifestyle factors on epigenetic aging, but also highlights significant heterogeneity between models and the need for careful consideration within study design.
Journal of the National Cancer Institute , article en libre accès, 2026