DNA methylation data-based prognosis-subtype distinctions in patients with esophageal carcinoma by bioinformatic studies

Esophageal carcinoma (ESCA) is caused by the accumulation of genetic and epigenetic alterations in esophageal mucosa. Of note, the earliest and the most frequent molecular behavior in the complicated pathogenesis of ESCA is DNA methylation. In the present study, we downloaded data of 178 samples from The Cancer Genome Atlas (TCGA) database to explore specific DNA methylation sites that affect prognosis in ESCA patients. Consequently, we identified 1,098 CpGs that were significantly associated with patient prognosis. Hence, these CpGs were used for consensus clustering of the 178 samples into seven clusters. Specifically, the samples in each group were different in terms of age, gender, tumor stage, histological type, metastatic status, and patient prognosis. We further analyzed 1,224 genes in the corresponding promoter regions of the 1,098 methylation sites, and enriched these genes in biological pathways with close correlation to cellular metabolism, enzymatic synthesis, and mitochondrial autophagy. In addition, nine representative specific methylation sites were screened using the weighted gene coexpression network analysis. Finally, a prognostic prediction model for ESCA patients was built in both training and validation cohorts. In summary, our study revealed that classification based on specific DNA methylation sites could reflect ESCA heterogeneity and contribute to the improvement of individualized treatment and precise prognostic prediction.

Automated summary

Esophageal carcinoma is characterized by genetic and epigenetic alterations, with DNA methylation being a key factor in its pathogenesis. By analyzing data from TCGA database, specific DNA methylation sites were identified to impact patient prognosis, leading to the classification of patients into seven clusters with distinct characteristics. Further analysis revealed genes and pathways associated with cellular metabolism and autophagy, and the construction of a prognostic prediction model for individualized treatment.