Dataset Information

Epigenetic clustering of gastric carcinomas based on DNA methylation profiles at the precancerous stage: its correlation with tumor aggressiveness and patient outcome.

ABSTRACT: The aim of this study was to clarify the significance of DNA methylation alterations during gastric carcinogenesis. Single-CpG resolution genome-wide DNA methylation analysis using the Infinium assay was performed on 109 samples of non-cancerous gastric mucosa (N) and 105 samples of tumorous tissue (T). DNA methylation alterations in T samples relative to N samples were evident for 3861 probes. Since N can be at the precancerous stage according to the field cancerization concept, unsupervised hierarchical clustering based on DNA methylation levels was performed on N samples (?N) using the 3861 probes. This divided the 109 patients into three clusters: A (n = 20), B1 (n = 20), and B2 (n = 69). Gastric carcinomas belonging to Cluster B1 showed tumor aggressiveness more frequently than those belonging to Clusters A and B2. The recurrence-free and overall survival rates of patients in Cluster B1 were lower than those of patients in Clusters A and B2. Sixty hallmark genes for which ?N characterized the epigenetic clustering were identified. We then focused on DNA methylation levels in T samples (?T) of the 60 hallmark genes. In 48 of them, including the ADAM23, OLFM4, AMER2, GPSM1, CCL28, DTX1 and COL23A1 genes, ?T was again significantly correlated with tumor aggressiveness, and the recurrence-free and/or overall survival rates. Multivariate analyses revealed that ?T was a significant prognostic factor, being independent of clinicopathological parameters. These data indicate that DNA methylation profiles at the precancerous stage may be inherited by gastric carcinomas themselves, thus determining tumor aggressiveness and patient outcome.

SUBMITTER: Yamanoi K

PROVIDER: S-EPMC4417340 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Epigenetic clustering of gastric carcinomas based on DNA methylation profiles at the precancerous stage: its correlation with tumor aggressiveness and patient outcome.

Yamanoi Kazuhiro K Arai Eri E Tian Ying Y Takahashi Yoriko Y Miyata Sayaka S Sasaki Hiroki H Chiwaki Fumiko F Ichikawa Hitoshi H Sakamoto Hiromi H Kushima Ryoji R Katai Hitoshi H Yoshida Teruhiko T Sakamoto Michiie M Kanai Yae Y

Carcinogenesis 20150304 5

The aim of this study was to clarify the significance of DNA methylation alterations during gastric carcinogenesis. Single-CpG resolution genome-wide DNA methylation analysis using the Infinium assay was performed on 109 samples of non-cancerous gastric mucosa (N) and 105 samples of tumorous tissue (T). DNA methylation alterations in T samples relative to N samples were evident for 3861 probes. Since N can be at the precancerous stage according to the field cancerization concept, unsupervised hi ...[more]

PMID: 25740824

Similar Datasets

Project description:The aim of this study was to clarify the significance of DNA methylation alterations at precancerous stages of lung adenocarcinoma. Using single-CpG resolution Infinium array, genome-wide DNA methylation analysis was performed in 36 samples of normal lung tissue obtained from patients without any primary lung tumor, 145 samples of non-cancerous lung tissue (N) obtained from patients with lung adenocarcinomas, and 145 samples of tumorous tissue (T). Stepwise progression of DNA methylation alterations from normal lung tissue to non-cancerous lung tissue obtained from patients with lung adenocarcinomas, and then tumorous tissue samples, was observed at 3,270 CpG sites, suggesting that non-cancerous lung tissue obtained from patients with lung adenocarcinomas was at precancerous stages with DNA methylation alterations. At CpG sites of 2,083 genes, DNA methylation status in samples of non-cancerous lung tissue obtained from patients with lung adenocarcinomas was significantly correlated with recurrence after establishment of lung adenocarcinomas. Among such recurrence-related genes, 28 genes are normally unmethylated (average ?-values based on Infinium assay in normal lung tissue samples was less than 0.2) and their DNA hypermethylation at precancerous stages was strengthened during progression to lung adenocarcinomas (??(T-N)>0.1). Among these 28 genes, we focused on 6 for which implications in transcription regulation, apoptosis or cell adhesion had been reported. DNA hypermethylation of the ADCY5, EVX1, GFRA1, PDE9A, and TBX20 genes resulted in reduced mRNA expression in tumorous tissue samples. 5-Aza-2'-deoxycytidine treatment of lung cancer cell lines restored the mRNA expression levels of these 5 genes. Reduced mRNA expression in tumorous tissue samples was significantly correlated with tumor aggressiveness. These data suggest that DNA methylation alterations at precancerous stages determine tumor aggressiveness and outcome through silencing of specific genes.

Project description:BackgroundDNA methylation occurs in the context of a CpG dinucleotide. It is an important epigenetic modification, which can be inherited through cell division. The two major types of methylation include hypomethylation and hypermethylation. Unique methylation patterns have been shown to exist in diseases including various types of cancer. DNA methylation analysis promises to become a powerful tool in cancer diagnosis, treatment and prognostication. Large-scale methylation arrays are now available for studying methylation genome-wide. The Illumina methylation platform simultaneously measures cytosine methylation at more than 1500 CpG sites associated with over 800 cancer-related genes. Cluster analysis is often used to identify DNA methylation subgroups for prognosis and diagnosis. However, due to the unique non-Gaussian characteristics, traditional clustering methods may not be appropriate for DNA and methylation data, and the determination of optimal cluster number is still problematic.MethodA Dirichlet process beta mixture model (DPBMM) is proposed that models the DNA methylation expressions as an infinite number of beta mixture distribution. The model allows automatic learning of the relevant parameters such as the cluster mixing proportion, the parameters of beta distribution for each cluster, and especially the number of potential clusters. Since the model is high dimensional and analytically intractable, we proposed a Gibbs sampling "no-gaps" solution for computing the posterior distributions, hence the estimates of the parameters.ResultThe proposed algorithm was tested on simulated data as well as methylation data from 55 Glioblastoma multiform (GBM) brain tissue samples. To reduce the computational burden due to the high data dimensionality, a dimension reduction method is adopted. The two GBM clusters yielded by DPBMM are based on data of different number of loci (P-value < 0.1), while hierarchical clustering cannot yield statistically significant clusters.

Dataset Information

Epigenetic clustering of gastric carcinomas based on DNA methylation profiles at the precancerous stage: its correlation with tumor aggressiveness and patient outcome.

Publications

Epigenetic clustering of gastric carcinomas based on DNA methylation profiles at the precancerous stage: its correlation with tumor aggressiveness and patient outcome.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets