Characterization of the Immune Infiltration Landscape and Identification of Prognostic Biomarkers for Esophageal Cancer

Abstract

Immunotherapy is an effective treatment for esophageal cancer (ESCA) patients. However, there are no dependable markers for predicting prognosis and immunotherapy responses in ESCA. Our study aims to explore immune gene prognostic models and markers in ESCA as well as predictors for immunotherapy. The expression profiles of ESCA were obtained from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and International Cancer Genome Consortium (ICGC) databases. Cox regression analysis was performed to construct an immune gene prognostic model. ESCA was grouped into three immune cell infiltration (ICI) clusters by CIBERSORT algorithm. The immunotherapy response of patients in different ICI score clusters was also compared. The copy number variations, somatic mutations, and single nucleotide polymorphisms were analyzed. Enrichment analyses were also performed. An immune gene prognostic model was successfully constructed. The ICI score may be used as a predictor independent of tumor mutation burden. Enrichment analyses showed that the differentially expressed genes were mostly enriched in microvillus and the KRAS and IL6/JAK/STAT3 pathways. The top eight genes with the highest mutation frequencies in ESCA were identified and all related to the prognosis of ESCA patients. Our study established an effective immune gene prognostic model and identified markers for predicting the prognosis and immunotherapy response of ESCA patients.

Activated Mast Cells Combined with NRF2 Predict Prognosis for Esophageal Cancer

“…Additionally, the expression of PD-L1 was highest in ICI gene cluster C. Hence, the patients in ICI gene cluster C might have a better immune response. Te outcome of our analysis was in accordance with the previous study, which indicated that ICI clusters and ICI gene clusters in EC might infuence the expression of PD-L1 [37].…”

“…The TGF- β /EMT signaling pathway influenced the expression of PD-L1 and promoted immune escape. All these results demonstrated that the ICI score was related to the PD-L1 but was not an independent prognosis marker for EC [ 37 ].…”

Characterization of the Immune Microenvironmental Landscape of Lung Squamous Cell Carcinoma with Immune Cell Infiltration

“…Given the diversity of the individual immunological milieu, quantifying the ICI models for individual malignancies is very relevant. In breast cancer, esophageal cancer, and the head and neck squamous cell carcinoma, the individualbased models based on tumor subtype-specific biomarkers have been well established to improve prognostic prediction [36][37][38]. In this study, we established an ICI score to quantify the ICI pattern.…”