Objective DNA methylation and histone modifications jointly regulate gene expression. In this study, co-regulation refers to their combined contribution to gene expression changes. We analyzed multi-omics data from HepG2 cells and in vitro differentiated hepatocytes. This study aimed to identify key DNA methylation sites and histone modifications. A further aim was to construct a co-regulation index to quantify their combined contribution on gene expression.Methods We collected ChIP-seq data for 11 histone modifications, whole-genome bisulfite sequencing data, and poly(A)+ RNA-seq data from the ENCODE database. Differentially expressed genes and differentially methylated CpG sites were identified by using DESeq2 and methylKit. The promoter regions were defined as 2 000 bp upstream and 2 000 bp downstream of the transcription start site. It was divided into 80 bins of 50 bp. Bins 3 to 12 flanking transcription start site were selected as candidate bins. The methylation differences between HepG2 cells and in vitro differentiated hepatocytes in these bins were evaluated with a Wilcoxon rank-sum test. Hypomethylated sites in up-regulated genes and hypermethylated sites in down-regulated genes were retained as key sites. A 1 000 bp region centered on each key site was divided into five 200 bp segments. Histone modification signals were calculated for each segment. DNA methylation differences and histone modification signal differences were used as features in the XGBoost classifier. We evaluated all 2 047 combinations of the 11 histone modifications and DNA methylation by five-fold cross-validation. We also constructed a co-regulation index. This index combined the change in each epigenetic feature with the direction and strength of its correlation with gene expression. The index values were converted to Z-scores. Regions with Z-scores above 0 were selected for further analysis. The stability of this classification was assessed by 1 000 bootstrap iterations. We then used RNA-seq and clinical data from patients with hepatocellular carcinoma in TCGA to analyze genes whose promoters contained these regions. Univariate Cox regression, LASSO regression, and multivariable Cox regression were used to identify survival-associated candidate genes.Results We identified 4 676 differentially expressed genes. Among them, 1 537 genes were up-regulated and 3 139 genes were down-regulated in HepG2 cells relative to in vitro differentiated hepatocytes. The selected promoter bins had higher mean absolute methylation differences than the remaining bins in the up-regulated gene group (p=1.49×10^-4). We identified 1 182 promoter hypomethylation sites in 495 up-regulated genes and 1 075 promoter hypermethylation sites in 1 083 down-regulated genes. Among the 11 histone modifications, the models combining DNA methylation with H3K27ac, H3K9ac, or H3K4me3 showed the highest predictive performance. A model containing these three histone modifications and DNA methylation achieved the AUC of 0.900 9. After the co-regulation index was added to the prediction model, the AUC increased from 0.900 9 to 0.928 7. A total of 882 regions with Z-scores above 0 were identified in the promoters of 322 genes. Bootstrap analysis showed a mean classification consistency of 99.38%. These genes were enriched in signal regulation, cell adhesion, tissue development, the PI3K-Akt signaling pathway, and ECM-receptor interaction. Among the 322 genes, 43 genes were differentially expressed in TCGA hepatocellular carcinoma samples. Seven genes were associated with survival in univariate Cox analysis. FBXW10 and B3GALT2 were retained after LASSO and multivariable Cox analyses. The two-gene risk model achieved AUC values of 0.759, 0.834, and 0.914 for 1-, 3-, and 5-year survival, respectively.Conclusion H3K4me3, H3K9ac, and H3K27ac showed the strongest co-regulation with DNA methylation in the hepatic cell models. The co-regulation index quantified the combined contribution of DNA methylation and histone modifications to gene expression changes. The incorporation of the co-regulation index increased the model AUC from 0.900 9 to 0.928 7. The index was used to identify key co-regulated regions. Further analysis with TCGA data identified FBXW10 and B3GALT2 as survival-associated candidate genes. These findings will be helpful for further analyzing and understanding the co-regulation of DNA methylation and histone modifications in cancer research.