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Background  In recent years, DNA methylation modification has been shown to be a critical mechanism in the field of epigenetics.    Methods  Hepatocellular carcinoma (HCC) data were obtained from The Cancer Genome Atlas project, including RNA expression profiles, Illumina Human Methylation 450K BeadChip data, clinical information, and pathological features. Then, differentially expressed genes (DEGs) and differentially methylated genes were identified using R software. Methylation-regulated DEGs (MeDEGs) were further analyzed using Spearman’s correlation analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the DAVID database and ClueGO in Cytoscape software. Kaplan–Meier survival analysis explored the relationship between methylation, expression of MeDEGs, and survival time. Gene set enrichment analysis (GSEA) was conducted to predict the function of prognosis-related MeDEGs.    Results  A total of nine up-regulated and 72 down-regulated MeDEGs were identified. GO and KEGG pathway analyses results indicated that multiple cancer-related terms were enriched. Kaplan–Meier survival analysis showed that the methylation status of four MeDEGs (CTF1, FZD8, PDK4, and ZNF334) was negatively associated with overall survival. Moreover, the methylation status of CDF1 and PDK4 was identified as an independent prognostic factor. According to GSEA, hypermethylation of prognosis-related MeDEGs was enriched in pathways that included “Spliceosome”, “Cell cycle”, “RNA degradation”, “RNA polymerase”, “DNA replication”, “Mismatch repair”, “Base excision repair”, “Nucleotide excision repair”, “Homologous recombination”, “Protein export”, and “Pyrimidine metabolism”.    Conclusions  Aberrant DNA methylation plays a critical role in malignant progression of HCC. Prognosis-related MeDEGs identified in this research may be potential biomarkers and targets in diagnosis and treatment.

frontiers in oncology

Identification of Methylation-Regulated Differentially Expressed Genes and Related Pathways in Hepatocellular Carcinoma: A Study Based on TCGA Database and Bioinformatics Analysis