Genome-Wide Analysis of Promoter Methylation Associated with Gene Expression Profile in Pancreatic Adenocarcinoma

Department of Pathology, Oncology, and Medicine, Bloomberg School of Public Health, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Johns Hopkins University, Baltimore, Maryland, USA.
Clinical Cancer Research (Impact Factor: 8.72). 05/2011; 17(13):4341-54. DOI: 10.1158/1078-0432.CCR-10-3431
Source: PubMed


The goal of this study was to comprehensively identify CpG island methylation alterations between pancreatic cancers and normal pancreata and their associated gene expression alterations.
We employed methylated CpG island amplification followed by CpG island microarray, a method previously validated for its accuracy and reproducibility, to analyze the methylation profile of 27,800 CpG islands covering 21 MB of the human genome in nine pairs of pancreatic cancer versus normal pancreatic epithelial tissues and in three matched pairs of pancreatic cancer versus lymphoid tissues from the same individual.
This analysis identified 1,658 known loci that were commonly differentially methylated in pancreatic cancer compared with normal pancreas. By integrating the pancreatic DNA methylation status with the gene expression profiles of the same samples before and after treatment with the DNA methyltransferase inhibitor 5-aza-2'-deoxycytidine, and the histone deacetylase inhibitor, trichostatin A, we identified dozens of aberrantly methylated and differentially expressed genes in pancreatic cancers including a more comprehensive list of hypermethylated and silenced genes that have not been previously described as targets for aberrant methylation in cancer.
We expected that the identification of aberrantly hypermethylated and silenced genes will have diagnostic, prognostic, and therapeutic applications.

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Available from: Noriyuki Omura
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    • "percent of human pancreatic tumors contain mutations in key members of the Wnt/β-catenin pathway, its crosstalk with other signaling pathways such as TGF-C, Hedgehog and Notch has been reported, thereby indirectly implicating its involvement in pancreatic cancer [29]. Indeed genetic alterations in pancreatic cancer can be linked to 12 core pathways and processes shared by all tumors, including the Wnt/β-catenin pathway [8] [30]. The data presented here support a role for ROBO3 crosstalk with the Wnt/β-catenin pathway in pancreatic cancer and we can speculate that ROBO3 promotes oncogenic transformation by inhibiting the Wnt/β-catenin signaling via SFRP binding. "
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    ABSTRACT: Pancreatic carcinoma is a highly lethal malignancy with an extremely poor prognosis. Recent genome-wide studies have implicated axon guidance pathways, including the SLIT/ROBO pathway, in pancreatic tumor development and progression. Here we showed that ROBO3 expression is up-regulated in pancreatic cancer tissue samples and cell lines. Over-expression of ROBO3 promotes pancreatic cancer cell growth, invasion and metastasis in vitro and in mouse xenograft tumor models. We identified miR-383 as a suppressor of ROBO3, and revealed its expression to be inversely correlated with ROBO3. Over-expression of ROBO3 activates Wnt pathway components, β-catenin and GSK-3, and the expression of markers indicating an EMT. By means of immunoprecipitation, we revealed an interaction between Wnt inhibitor SFRP and ROBO3 in pancreatic cancer cell lines. Our work suggests that ROBO3 may contribute to the progression of pancreatic cancer by sequestering Wnt inhibitor SFRP, which in turn leads to increased Wnt/β-catenin pathway activity. We also confirmed that ROBO3 increases with clinical grade and miR-383 expression is inversely correlated to that of ROBO3. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
    Full-text · Article · Jun 2015 · Cancer letters
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    • "We also compared our aberrant methylated gene targets determined in this study with the results of previous studies. Among all 3,911 differently methylated genes (DMGs) identified in this study, 728 DMGs were reported by Nourse et al.[36], 339 DMGs were reported by Vincent et al.[7], and 55 DMGs were reported by Tan et al. [37] (Additional file 18: Table S17). This discrepancy in the number of DMGs obtained between the four groups might result from the different technological platforms adopted by each study group, as well as the different ethnic backgrounds of the enrolled patients. "
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    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.
    Full-text · Article · Sep 2014 · Clinical Epigenetics
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    • "Methylation sites have been analyzed based on clustering with respect to genomic regions, methylation patterns, and common regulatory patterns [16]. Increased methylation of CpG islands in the promoter regions known as hypermethylation leads to silencing of genes, usually associated with tumor suppressor genes [18], whereas the decreased methylation known as hypomethylation is associated with gene overexpression i.e., activation of oncogenes [18]. Both hypermethylation and hypomethylation are known to be linked to tumors, autoimmune and other diseases [16,19,20]. "
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    ABSTRACT: Epigenetics refers to the reversible functional modifications of the genome that do not correlate to changes in the DNA sequence. The aim of this study is to understand DNA methylation patterns across different stages of lung adenocarcinoma (LUAD). Our study identified 72, 93 and 170 significant DNA methylated genes in Stages I, II and III respectively. A set of common 34 significant DNA methylated genes located in the promoter section of the true CpG islands were found across stages, and these were: HOX genes, FOXG1, GRIK3, HAND2, PRKCB, etc. Of the total significant DNA methylated genes, 65 correlated with transcription function. The epigenetic analysis identified the following novel genes across all stages: PTGDR, TLX3, and POU4F2. The stage-wise analysis observed the appearance of NEUROG1 gene in Stage I and its re-appearance in Stage III. The analysis showed similar epigenetic pattern across Stage I and Stage III. Pathway analysis revealed important signaling and metabolic pathways of LUAD to correlate with epigenetics. Epigenetic subnetwork analysis identified a set of seven conserved genes across all stages: UBC, KRAS, PIK3CA, PIK3R3, RAF1, BRAF, and RAP1A. A detailed literature analysis elucidated epigenetic genes like FOXG1, HLA-G, and NKX6-2 to be known as prognostic targets. Integrating epigenetic information for genes with expression data can be useful for comprehending in-depth disease mechanism and for the ultimate goal of better target identification.
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