Abstract
Esophageal cancer is a highly malignant disease with poor prognosis. Despite recent advances in the study of esophageal cancer, there has been only limited improvement in the treatment and prognosis. N6-methyladenosine (m6A), a type of RNA modification, has been extensively investigated and is involved in many biological behaviors, including tumorigenesis and progression. Thus, more research on m6A modification may increase our understanding of esophageal cancer pathogenesis and provide potential targets. In our study, we integrated the public data of esophageal cancer from The Cancer Genome Atlas (TCGA) and Gene-Expression Omnibus (GEO) databases. Unsupervised clustering analysis was used to classify patients into different groups. Gene set variation analysis (GSVA) was performed in a nonparametric and unsupervised mode. We evaluated immune cell infiltration by single sample gene set enrichment analysis (ssGSEA). Differentially expressed genes (DEGs) among m6A clusters were identified using Empirical Bayesian approach. Both multivariate and univariate Cox regression models were used for prognostic analysis. We provided an overview of gene variation and expression of 23 m6A regulators in esophageal cancer, as well as their effects on survival. Based on the overall expression level of m6A regulators, patients were classified into three m6A clusters (A-C) with different immune cell infiltration abundance, gene expression signatures and prognosis. Among m6A clusters, we identified 206 DEGs, according to which patients were classified into 4 gene clusters (A-D). Quantitative m6A score was calculated for each patient based on those DEGs with significant impact on survival. The infiltration of all types of immune cells except type 2 T helper (Th2) cells were negatively correlated with m6A score. M6Acluster C exhibited the lowest m6A score, the most abundant immune cell infiltration, and the worst prognosis, suggesting an immune excluded phenotype. Consistently, gene cluster D with the lowest m6A score showed the worst prognosis. In short, patients with esophageal cancer showed different m6A modification patterns. Quantitative scoring indicated that patients with the lowest m6A score exhibited the most abundant immune cell infiltration and the poorest prognosis. This m6A scoring system is promising to assess m6A modification pattern, characterize immune infiltration and guide personalized treatment and prognostic prediction.