AUTHOR=Guo Xiong , Wang Yujun , Zhang Han , Qin Chuan , Cheng Anqi , Liu Jianjun , Dai Xinglong , Wang Ziwei 

TITLE=Identification of the Prognostic Value of Immune-Related Genes in Esophageal Cancer

JOURNAL=Frontiers in Genetics

VOLUME=Volume 11 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2020.00989

DOI=10.3389/fgene.2020.00989

ISSN=1664-8021

ABSTRACT=Esophageal cancer (EC) is a serious malignant tumor, both in terms of mortality and prognosis, and immune-related genes (IRGs) are key contributors to its development. In recent years, immunotherapy for tumors has been widely studied, but a practical prognostic model based on immune-related genes (IRGs) in EC has not been established and reported. In this study, we downloaded the gene expression profiles and matched clinical data of EC patients from The Cancer Genome Atlas (TCGA) database. We found 4,094 differentially expressed genes (DEGs) between EC and normal esophageal tissue (p<0.05 and fold change >2). Then, the intersection of DEGs and the immune genes in the ‘ImmPort’ database resulted in 303 differentially expressed immune-related genes (DEIRGs). Next, through univariate Cox regression analysis of DEIRGs, we obtained 17 immune genes related to prognosis. We detected nine optimal prognostic-associated IRGs (HSPA6, CACYBP, DKK1, EGF, FGF19, GAST, OSM, ANGPTL3, NR2F2) by Using Lasso regression and multivariate Cox regression analyses. Finally, we used those prognostic-associated IRGs to construct a risk model to predict the prognosis of EC patients. This model could accurately predict overall survival in EC and could be used as a classifier for the evaluation of low-risk and high-risk groups. In conclusion, we identified a practical and robust nine-gene prognostic model based on immune gene dataset. These genes may provide valuable biomarkers and prognostic predictors for EC patients and could be further studied to help understand the mechanism of EC occurrence and development.