The alteration of DNA methylation landscape is a key epigenetic event in cancer. As the accumulation of large-scale genome-wide DNA methylation data from clinical samples, we are able to characterize the patterns of DNA methylation alterations for identifying candidate epigenetic markers and drivers. In this survey, we take hepatocellular carcinoma (HCC) as an example to show the basic steps of analyzing the DNA methylation patterns in cancer across multiple data sets. We collected three genome-wide DNA methylation data sets with xe2x88xbc800 clinical samples and the corresponding gene expression data sets. First, by quantitatively analyzing two global methylation alterations, it is found that about 90% tumors acquire either genome-wide DNA hypo-methylation or CpG island methylator phenotype. Second, probe-level analysis identified 267, 228 and 197 hyper-methylated sites in promoter regions for the three data sets, respectively. These local hyper-methylated patterns are highly consistent: 84 sites (from 61 promoters) are hyper-methylated in all the three studied data sets, including many previously reported genes, such as CDKL2, TBX15 and NKX6-2. Then, these hyper-methylated sites were used as candidate markers to classify tumor and non-tumor samples. The classifiers based on only 10 selected probes can achieve high discriminative ability across different data sets. Finally, by integrative analyzing DNA methylation and gene expression data, we identified 222 candidate epigenetic drivers, which are enriched in inflammatory response and multiple metabolic pathways. A set of high-confidence candidates, including SFN, SPP1 and TKT, are significantly associated with patients overall survivals. In summary, this study systematically characterized the DNA methylation alterations and their impacts on gene expressions in HCCs based on multiple data sets.