Development and validation of a prognostic model for esophageal carcinoma based on immune microenvironment using system bioinformatics.

Esophageal cancer (EC) is an aggressive malignancy that accounts for numerous cancer-related deaths worldwide. The multimodal combination therapy approach can be potentially used to treat EC effectively. However, distinct biomarker of significant specificity are still needed to develop individualized treatment strategies and provide accurate prognostic predictions. Therefore, we aimed to investigate the associated genes subtypes identified were, IFN-γDominant, Inflammatory, Lymphocyte Depleted, etc. and construct a risk model based on these genes to predict the overall survival (OS) of patients suffering from EC. Three immune subtypes were defined in the Cancer Genome Atlas (TCGA) cohort with different tumor microenvironment (TME) and clinical outcomes based on radio-differentiated immune genes. Subsequently, a risk model of immune characteristics included the immune cell infiltration levels and pathway activity was developed based on the genomic changes between the subtypes. In the TCGA dataset, as well as in subgroup analysis with different stages, gender, age, and pathological type, a high-risk score was identified as an adverse factor for OS using the method of the univariate Cox regression analysis and tROC analysis. Furthermore, it was observed that the high-risk group was characterized by depleted immunophenotype, active cell metabolism, and a high tumor mutation burden (TMB). The low-risk group was characterized by high TME abundance and active immune function. Differences in the biological genotypes may account for the differences in the prognosis and treatment response. Extensive research was carried out, and the results revealed that the low-risk group exhibited a significant level of therapeutic advantage in the field of immunotherapy. A risk model was developed based on the immune characteristics. It can be used to optimize risk stratification for patients suffering from EC. The results can potentially help provide new perspectives on treatment methods.