biostudies-literatureAntwi SONational Institutes of Health National Cancer InstituteNational Cancer Institute Division of Cancer Epidemiology and GeneticsIpsen BiopharmaceuticalsNCI NIH HHSNational Institutes of HealthCancer Prevention and Research Institute of Texas100621https://www.ebi.ac.uk/biostudies/studies/S-EPMC12019016biostudies-literatureUnknown4(5)<h4>Background and aims</h4>Metabolic liver disease is the fastest-rising cause of hepatocellular carcinoma (HCC), but the underlying molecular processes that drive HCC development in the setting of metabolic perturbations are unclear. We investigated the role of aberrant DNA methylation in metabolic HCC development in a multicenter international study.<h4>Methods</h4>We used a case-control design, frequency-matched on age, sex, and study site. Genome-wide profiling of peripheral blood leukocyte DNA was performed using the 850k EPIC array. The study sample was split 80% and 20% for training and validation. Cell type proportions were estimated from the methylation data. Differential methylation analysis was performed adjusting for cell type, generating area under the receiver-operating charGastro hep advancesGenome-Wide DNA Methylation Markers Associated With Metabolic Liver Cancer.PMC12019016RP200554R37 CA259201P50 CA210964K01 CA237875U01 CA271887U01 CA230694Boix LGomez MRSingal AGAntwi SOWangensteen KJRojas AJnr Siaw ADArmasu SMStal PRoberts LRBanales JMReig MYan IKPatel TFrank JAIzquierdo-Sanchez LAhmed FYfalseGenome-Wide DNA Methylation Markers Associated With Metabolic Liver Cancer.<h4>Background and aims</h4>Metabolic liver disease is the fastest-rising cause of hepatocellular carcinoma (HCC), but the underlying molecular processes that drive HCC development in the setting of metabolic perturbations are unclear. We investigated the role of aberrant DNA methylation in metabolic HCC development in a multicenter international study.<h4>Methods</h4>We used a case-control design, frequency-matched on age, sex, and study site. Genome-wide profiling of peripheral blood leukocyte DNA was performed using the 850k EPIC array. The study sample was split 80% and 20% for training and validation. Cell type proportions were estimated from the methylation data. Differential methylation analysis was performed adjusting for cell type, generating area under the receiver-operating char2025-01-01T00:00:00Z20252026-06-01T21:06:57.208Z2026-05-22T03:07:30.302ZS-EPMC120190164027593310.1016/j.gastha.2025.100621