Comprehensive Genome-Scale Analysis of Esophageal Carcinoma With Esophageal Tissue-Resident Micro-Environment Discrepancy

Abstract

To figure out the molecular mechanism in the esophageal squamous carcinoma (ESCC) with the discrepancy in the tissue-resident microbiota, we selected clinical features, RNA sequences, and transcriptomes of ESCC patients from The Cancer Genome Atlas (TCGA) website and detailed tissue-resident microbiota information from The Cancer Microbiome Atlas (n = 60) and explored the infiltration condition of particular microbiota in each sample. We classified the tissue-resident micro-environment of ESCC into two clusters (A and B) and built a predictive classifier model. Cluster A has a higher proportion of certain tissue-resident microbiota with comparatively better survival, while Cluster B has a lower proportion of certain tissue-resident microbiota with comparatively worse survival. We showed traits of gene and clinicopathology in the esophageal tissue-resident micro-environment (ETM) phenotypes. By comparing the two clusters' molecular signatures, we find that the two clusters have obvious differences in gene expression and mutation, which lead to pathway expression discrepancy. Several pathways are closely related to tumorigenesis. Our results may demonstrate a synthesis of the infiltration pattern of the esophageal tissue-resident micro-environment in ESCC. We reveal the mechanism of esophageal tissue-resident microbiota discrepancy in ESCC, which may contribute to therapy progress for patients with ESCC.

Keywords: LASSO analysis; R language software; esophageal squamous carcinoma; esophageal tissue-resident micro-environment; tissue-resident flora.