Epigenetic study of esophageal carcinoma based on methylation, gene integration and weighted correlation network analysis

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Abstract

Purpose: Esophageal carcinoma is a common and highly metastatic malignant tumor of the digestive tract. The aim of the present study was to identify potential molecular markers of esophageal carcinoma that may help its diagnosis and treatment. Materials and Methods: First, mRNA and DNA methylation data were downloaded from The Cancer Genome Atlas (TCGA) database for the identification of differentially expressed genes (DEGs) and DNA methylation analysis. Secondly, Weighted Gene Co-Expression Network Analysis (WGCNA) was used to identify important modules and hub genes. In addition, correlation analysis between DNA methylation genes and DEGs was performed. Thirdly, the GSE45670 dataset was used to validate the expression of the diagnostic and survival ability analysis of genes in TCGA data. Finally, reverse transcription-quantitative PCR and immunohistochemical analysis of genes were performed. Results: A total of 2408 DEGs and 5134 differentially methylated sites were obtained. In the WGCNA analysis, the royal blue module was found to be the optimal module. In addition, hub genes in the module, including ESRRG, MFSD4, CCKBR, ATP4B, ESRRB, ATP4A, CCKAR and B3GAT1, were also differentially methylated genes and DEGs. It was found that CCKAR, MFSD4 and ESRRG may be diagnostic gene biomarkers for esophageal carcinoma. In addition, the high expression of MFSD4 was significantly correlated with patient survival. Immunohistochemistry analysis results showed that the gene expression levels of ATP4B, B3GAT1, CCKBR and ESRRG were decreased in esophageal carcinoma tissues, which was in line with the bioinformatics results. Conclusion: Therefore, these identified molecular markers may be helpful in the diagnosis and treatment of esophageal carcinoma.

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Xu, Y., Wang, N., Liu, R., Lv, H., Li, Z., Zhang, F., … Tian, Z. (2021). Epigenetic study of esophageal carcinoma based on methylation, gene integration and weighted correlation network analysis. OncoTargets and Therapy, 14, 3133–3149. https://doi.org/10.2147/OTT.S298620

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