Dataset Information

Network-based analysis identifies epigenetic biomarkers of esophageal squamous cell carcinoma progression.

ABSTRACT:

Motivation

A rapid progression of esophageal squamous cell carcinoma (ESCC) causes a high mortality rate because of the propensity for metastasis driven by genetic and epigenetic alterations. The identification of prognostic biomarkers would help prevent or control metastatic progression. Expression analyses have been used to find such markers, but do not always validate in separate cohorts. Epigenetic marks, such as DNA methylation, are a potential source of more reliable and stable biomarkers. Importantly, the integration of both expression and epigenetic alterations is more likely to identify relevant biomarkers.

Results

We present a new analysis framework, using ESCC progression-associated gene regulatory network (GRN escc), to identify differentially methylated CpG sites prognostic of ESCC progression. From the CpG loci differentially methylated in 50 tumor-normal pairs, we selected 44 CpG loci most highly associated with survival and located in the promoters of genes more likely to belong to GRN escc. Using an independent ESCC cohort, we confirmed that 8/10 of CpG loci in the promoter of GRN escc genes significantly correlated with patient survival. In contrast, 0/10 CpG loci in the promoter genes outside the GRN escc were correlated with patient survival. We further characterized the GRN escc network topology and observed that the genes with methylated CpG loci associated with survival deviated from the center of mass and were less likely to be hubs in the GRN escc. We postulate that our analysis framework improves the identification of bona fide prognostic biomarkers from DNA methylation studies, especially with partial genome coverage.

SUBMITTER: Cheng CP

PROVIDER: S-EPMC4609006 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Publications

Network-based analysis identifies epigenetic biomarkers of esophageal squamous cell carcinoma progression.

Cheng Chun-Pei CP Kuo I-Ying IY Alakus Hakan H Frazer Kelly A KA Harismendy Olivier O Wang Yi-Ching YC Tseng Vincent S VS

Bioinformatics (Oxford, England) 20140710 21

<h4>Motivation</h4>A rapid progression of esophageal squamous cell carcinoma (ESCC) causes a high mortality rate because of the propensity for metastasis driven by genetic and epigenetic alterations. The identification of prognostic biomarkers would help prevent or control metastatic progression. Expression analyses have been used to find such markers, but do not always validate in separate cohorts. Epigenetic marks, such as DNA methylation, are a potential source of more reliable and stable bio ...[more]

PMID: 25015989

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Project description:The poor outcome of patients with esophageal squamous cell carcinoma (ESCC) highlights the importance of the identification of novel effective prognostic biomarkers. Long non‑coding RNAs (lncRNAs) serve regulatory roles in various types of cancer. The aim of the present study was to investigate the lncRNA expression profile in ESCC and to identify lncRNAs associated with the prognosis of ESCC by performing comprehensive bioinformatics analyses. The RNA‑sequencing (Seq) expression dataset GSE53625 generated from ESCC samples was used as a training dataset. Additional RNA‑Seq datasets relative to ESCC samples were downloaded from The Cancer Genome Atlas and used as a validation dataset. Data were screened using the limma package, and differentially expressed lncRNAs between early‑ and late‑stage ESCC were identified. A random forest algorithm was used to select the optimal lncRNA biomarkers, which were then analyzed using the support vector machine (SVM) algorithm with R software. The identified lncRNA biomarkers were examined in the validation dataset by bidirectional hierarchical clustering and using an SVM classifier. Subsequently, univariate and multivariate Cox regression analyses were performed to analyze the potential ability lncRNAs to predict the survival rate of patients with ESCC. By examining the training group, 259 deregulated lncRNAs between early‑ and advanced‑stage ESCC were identified. Further bioinformatics analyses identified a nine‑lncRNA signature, including AC098973, AL133493, RP11‑51M24, RP11‑317N8, RP11‑834C11, RP11‑69C17, LINC00471, LINC01193 and RP1‑124C. This nine‑lncRNA signature was used to predict the tumor stage and patient survival rate with high reliability and accuracy in the training and validation datasets. Furthermore, these nine lncRNA biomarkers were primarily involved in regulating the cell cycle and DNA replication, and these processes were previously identified to be associated with the progression of ESCC. The identified nine‑lncRNA signature was identified to be associated with the tumor stage, and could be used as predictor of the survival rate of patients with ESCC.

Dataset Information

Network-based analysis identifies epigenetic biomarkers of esophageal squamous cell carcinoma progression.

Motivation

Results

Publications

Network-based analysis identifies epigenetic biomarkers of esophageal squamous cell carcinoma progression.

Similar Datasets

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