Development of a prognostic model for esophageal cancer based on nine immune related genes

Background: Function of the immune system is correlated with the prognosis of the tumor. The effect of immune microenvironment on esophageal cancer (EC) development has not been fully investigated. This study aimed to explore a prognostic model based on immune-related genes (IRGs) for EC. Methods: We obtained the RNA-seq dataset and clinical information of EC from the Cancer Genome Atlas (TCGA). We identified 247 upregulated IRGs and 56 downregulated IRGs. Pathway analysis revealed that the most differentially expressed IRGs were enriched in Cytokine-cytokine receptor interaction. We further screened 13 survival-related IRGs and constructed regulatory networks involving related transcription factors (TFs). Finally, a prognostic model was constructed with 9 IRGs (HSPA6, S100A12, CACYBP, NOS2, DKK1, OSM ,STC2 ,NGPTL3 and NR2F2) by multivariate Cox regression analysis. Results: The patients were classified into two subgroups with different outcomes. When adjusted with clinical factors, this model was verified as an independent predictor, which performed accurately in prognostic prediction. Next, M0 and M2 macrophages and activated mast cells were significantly enriched in high-risk group, while CD8 T cells and regulatory T cells (Tregs) were significantly enriched in low-risk group. Conclusion: These molecules may serve as potential therapeutic targets and biomarkers for the new-immunotherapy of EC.