Evaluating the Consistency of Gene Methylation in Liver Cancer Using Bisulfite Sequencing Data

Abstract

Bisulfite sequencing is considered as the gold standard approach for measuring DNA methylation, which acts as a pivotal part in regulating a variety of biological processes without changes in DNA sequences. In this study, we introduced the most prevalent methods for processing bisulfite sequencing data and evaluated the consistency of the data acquired from different measurements in liver cancer. Firstly, we introduced three commonly used bisulfite sequencing assays, i.e., reduced-representation bisulfite sequencing (RRBS), whole-genome bisulfite sequencing (WGBS), and targeted bisulfite sequencing (targeted BS). Next, we discussed the principles and compared different methods for alignment, quality assessment, methylation level scoring, and differentially methylated region identification. After that, we screened differential methylated genes in liver cancer through the three bisulfite sequencing assays and evaluated the consistency of their results. Ultimately, we compared bisulfite sequencing to 450 k beadchip and assessed the statistical similarity and functional association of differentially methylated genes (DMGs) among the four assays. Our results demonstrated that the DMGs measured by WGBS, RRBS, targeted BS and 450 k beadchip are consistently hypo-methylated in liver cancer with high functional similarity.

Keywords: DNA methylation; liver cancer; reduced-representation bisulfite sequencing; targeted bisulfite sequencing; whole-genome bisulfite sequencing.

FIGURE 1

Procedures for analyzing bisulfite sequencing data.

FIGURE 2

Hypo and hyper methylated genes in GSE70090, GSE112221, GSE55752, and TCGA (A–C). Heat map of 18 common hypo-methylated genes in four datasets (D–G).

FIGURE 3

(A) Ranking of the 18 DMGs by topological importance. (B) Enrichment analysis of the 18 genes. (C) Enriched KEGG pathways for the DMGs of each of the four datasets. (D) Enriched KEGG pathways for the DMGs exclusive in each dataset.

FIGURE 4

(A) Semantic similarity of all the differential methylated genes from four datasets. (B) Semantic similarity of the differential methylated genes unique in four datasets. (C) Density of semantic similarity of two genes randomly picked up from two datasets. (D) Protein-protein association network of four datasets. (E) Connection across the four datasets. (F) Functional association between four datasets.