Supplemental_DataSheet1.

Blood-based testing represents a valuable tool for the detection and monitoring of patient conditions in both human and veterinary medicine. When conventional tissue-based diagnosis is challenging, blood-derived measurements allow for minimally invasive testing. Recent studies across mammalian species, particularly in humans, have explored the use of DNA methylation from whole blood, revealing its potential to predict individual mortality and responses to environmental stresses. While it is well recognized that tumor lesions display altered epigenetic modifications across some mammalian species, little is known about how DNA methylation in blood, as an indirect tissue sample, reflects the status of individuals in dogs. In this study, we conducted whole genome bisulfite sequencing using whole blood samples from twenty dogs diagnosed with canine gastrointestinal lymphoma, which is a prevalent disease in dogs. Comparative analysis with non-lymphoma controls identified over one thousand differentially methylated regions (DMRs). To develop practical predictive models, we narrowed down the number of DMRs from the total identified to a feasible set of probes using machine learning, achieving high accuracy (0.8–0.9) in predicting lymphoma cases. Our research underscores the potential of utilizing DNA methylation from whole blood as predictors and establishes a foundational data infrastructure for genome-wide DNA methylation for canine health monitoring for future studies.