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

Chun Pei Cheng, I. Ying Kuo, Hakan Alakus, Kelly A. Frazer, Olivier Harismendy, Yi Ching Wang, Vincent S. Tseng

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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 (GRNescc), 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 GRNescc. Using an independent ESCC cohort, we confirmed that 8/10 of CpG loci in the promoter of GRNescc genes significantly correlated with patient survival. In contrast, 0/10 CpG loci in the promoter genes outside the GRNescc were correlated with patient survival. We further characterized the GRNescc 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 GRNescc. We postulate that our analysis framework improves the identification of bona fide prognostic biomarkers from DNA methylation studies, especially with partial genome coverage.

Original languageEnglish
Pages (from-to)3054-3061
Number of pages8
Issue number21
Publication statusPublished - 2014 May 28

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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