Abstract
Abstract Background Esophageal adenocarcinoma (EAC) is one of the most deadly tumors in the world, owing to its aggressive nature and late detection. Barrett’s esophagus (BE), a premalignant condition, is the only known precursor of EAC. We focused on finding epigenetic changes such as differentially methylated regions within EAC and BE patients to demonstrate their biological significance in relation to patient stratification and early detection. Methods A differential methylation analysis pipeline was used to identify significant CpGs between 23 EAC, 62 BE, 49 normal squamous (SQ) and 7 fundus samples (GSE81334). These CpGs of interest were validated in the second dataset from an independent cohort (GSE104707) and the TCGA ESCA project dataset. RNASeq and methylation datasets from TCGA were integrated to find overlapping biomarkers that may reveal biological control of methylation. We further performed GO and pathway analysis of differentially methylated CpGs and cnetplot, correlating the obtained CpGs with affected genes and Gene Ontologies and pathways. Results We identified and validated 505 differentially methylated CpGs. Using the obtained markers, we were able to separate the normal samples from the EAC samples. BE samples were divided into 2 distinct sub-classes clustering either with normal or EAC, indicating potential for early patient stratification. With the integrated analysis of differentially methylated probes and gene expression, 108 regions were identified to be correlated, indicating epigenetic control of the regions. GO enrichment analysis revealed these regions to belong to biological processes related to cancer development. Further integration of mutation signatures revealed this signal to be independent of known alterations. Conclusion(s): Methylation based clustering reveals markers showing distinct biological signals in BE and EAC patients and provides methylation status of distinct BE and EAC subtypes. Identified biomarkers show their relevance in early cancer detection and may exhibit strong potential in BE prognosis and better patients’ stratification. Characterization of these subtypes could aid in choosing more appropriate management strategies and better outcomes and improved survival of the patients.
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