Prediction of Biomarkers for Hepatocellular Carcinoma Through Microarray-Based DNA Methylation Analysis

Aberrant DNA methylation of cytosine guanine dinucleotide sides (CpGs) is one of the earliest and most frequent alterations in cancer. However, there is no complete understanding of the methylome in hepatocellular carcinoma (HCC), and few studies comprehensively evaluated methylation signatures of HCC based on high-throughput platforms.
Based on the DNA methylation data of HCC, the current study aimed at identifying the specific DNA methylation biomarkers to diagnose HCC.
The current study used bioinformatics method based on the published microarray data of HCC was implemented in Linyi, Shandong Province, China in 2017. Using GSE57956 data downloaded from the Gene Expression Omnibus database, the differentially methylated genes between HCC and normal groups were identified. Next, hierarchical clustering was conducted to measure whether the differentially methylated genes could distinguish the HCC from the normal samples. Furthermore, functional enrichment analyses were respectively implemented for up- and down-methylated genes to further extract the potential biological processes based on DAVID tool.
According to the cutoff threshold of ≥ 0.2 average beta-values difference, 1340 differentially methylated genes (1660 CpGs) were identified including 978 up-methylated and 682 down-methylated genes. Utilization of the up-methylated and down-methylated genes to enrich gene oncology (GO) terms and biological pathways led to the identification of several important function regions, and the most significant ones were innate immune response, transcription from RNA polymerase II promoter and neuroactive ligand-receptor interaction.
Functional terms (innate immune response, transcription from RNA polymerase II promoter, and neuroactive ligand-receptor interaction) might be as potential targets for HCC diagnosis and treatment.