High-frequency aberrantly methylated targets in pancreatic adenocarcinoma identified via global DNA methylation analysis using methylCap-seq

Abstract

Background: Extensive reprogramming and dysregulation of DNA methylation is an important characteristic of pancreatic cancer (PC). Our study aimed to characterize the genomic methylation patterns in various genomic contexts of PC. The methyl capture sequencing (methylCap-seq) method was used to map differently methylated regions (DMRs) in pooled samples from ten PC tissues and ten adjacent non-tumor (PN) tissues. A selection of DMRs was validated in an independent set of PC and PN samples using methylation-specific PCR (MSP), bisulfite sequencing PCR (BSP), and methylation sensitive restriction enzyme-based qPCR (MSRE-qPCR). The mRNA and expressed sequence tag (EST) expression of the corresponding genes was investigated using RT-qPCR.

Results: A total of 1,131 PC-specific and 727 PN-specific hypermethylated DMRs were identified in association with CpG islands (CGIs), including gene-associated CGIs and orphan CGIs; 2,955 PC-specific and 2,386 PN-specific hypermethylated DMRs were associated with gene promoters, including promoters containing or lacking CGIs. Moreover, 1,744 PC-specific and 1,488 PN-specific hypermethylated DMRs were found to be associated with CGIs or CGI shores. These results suggested that aberrant hypermethylation in PC typically occurs in regions surrounding the transcription start site (TSS). The BSP, MSP, MSRE-qPCR, and RT-qPCR data indicated that the aberrant DNA methylation in PC tissue and in PC cell lines was associated with gene (or corresponding EST) expression.

Conclusions: Our study characterized the genome-wide DNA methylation patterns in PC and identified DMRs that were distributed among various genomic contexts that might influence the expression of corresponding genes or transcripts to promote PC. These DMRs might serve as diagnostic biomarkers or therapeutic targets for PC.

Keywords: CGI shore; DNA methylation; genome-wide; methyl capture sequencing; orphan CGI; pancreatic adenocarcinoma.

Figure 1

Data mining of MethylCap-seq libraries. (A) Experimental strategy to evaluate differential DNA methylation in PC compared with PN. (B) Chromosomal view of genome-wide distribution of hypermethylated DNA in PC compared with PN. Red bar, hypermethylation in PC; green bar, hypermethylation in PN. (C) Hypermethylated peaks around the TSS site in PC compared with those in PN. Peaks were surveyed in a broad region (from 5 kb downstream to 5 kb upstream of the TSS). (D) Mapping peaks and differently methylated regions (DMRs) that were specific for PC and PN. The DMRs are shown according to their inclusion in different gene structure context, such as refGene or CpG island (CGI) definitions. Note that the y-axis is interrupted to show whole dataset. (E) Genomic distribution of DMRs with PC and PN in transcription start sites (TSSs), intragenic regions, and intergenic regions. The total number of DMRs is presented at the top of each graph. (F) DMR distribution over the various gene structures based on sole refGene involvement versus both CGI and refGene involvement in PC-specific and PN-specific DMRs. The genomic context is defined as that found in the UCSC database. (G) PC- and PN-selective DMR distribution over orphan CGIs versus refGene-related CGIs, and over CGI-containing promoters vs. no-CGI promoters. (H) DMRs (and their related genes) in PC and PN, considering the involvement of various CGI features (CGI, CGI shore, or both). CGI, CpG island; DMR, differently methylated regions. PC, pancreatic cancer; PN, adjacent non-tumor tissue; TSS, transcription start site.

Figure 2

Representative results of bisulfite sequencing PCR (BSP) and methylation-specific PCR (MSP) validation of methylCap-seq data. For each gene, the UCSC scheme of the gene locus and the examined promoter regions are shown. (A) BSP results. (B) MSP results. 15 pairs of PC and PN samples (1,2,3,4,5,6,7,8,9,10,11,12, 307,311,313) and an extra 1 PC (314) were evaluated. All the samples were assayed by MSP. GAPDH: GAPDH-BSP were amplified as quality and quantity control for the confirmation of bisulfite-converted DNA templates. N, negative control; P, positive control; PC, pancreatic cancer; PN, adjacent non-tumor tissue.

Figure 3

Methylation of CGIs (orphan CGIs or regular CGIs) might influence the expression of putative ESTs or mRNAs. (A) UCSC scheme of CGIs and the nearby putative ESTs or mRNAs analyzed in this study. (B) DNA methylation changes in PC cell lines after treatment with 5-aza-dc. The GAPDH-BSP product serves as a quality and quantity control for the bisulfite-converted DNA templates. (C) EST expression after 5-aza-dc treatment determined by RT-qPCR. GAPDH mRNA expression was the loading control. (D) Quantitative analysis of DNA methylation by methylation sensitive restriction enzyme-based qPCR (MSRE-qPCR) in eight PC and five PN samples. The box is defined by 25% and 75% quantiles. The methylation levels in the PC and PN samples were compared by one-way analysis of variance (ANOVA), and the P values are indicated. 5-AZ, 5-aza-2′-deoxycytidine; DMSO, dimethyl sulfoxide; EST, expressed sequence tag; N, negative control; P, positive control; PC, pancreatic cancer; PN, PN, adjacent non-tumor tissue.