Analysis of the tumor immune environment identifies an immune gene set–based prognostic signature in non-small cell lung cancer

BACKGROUND: The tumor immune environment plays a critical role in lung cancer initiation and prognosis. Therefore, understanding how the tumor immune environment impacts the overall survival (OS) of patients with advanced lung cancer post immunotherapy is of great importance. In this article, we aimed to identify the immune components of lung cancer and develop an immune prognostic signature to predict OS. METHODS: Differentially expressed immune-related genes were calculated between tumor and normal tissues using expression data from The Cancer Genome Atlas (TCGA) database. Then univariate Cox regression analysis was conducted to select prognosis-related genes and the prognostic risk model was constructed by multivariate Cox regression analysis. Patient risk scores were calculated, and a clinical correlation analysis was performed within the risk model. In addition, immune cell infiltration patterns were identified to find the immune cell subtypes related to prognosis RESULTS: A gene model consisting of 12 immune-related genes was used as our signature. The model showed that the high-risk group experienced a shorter survival time, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.733. High-risk immune genes, such as S100 calcium binding protein A16 (S100A16) and angiopoietin-like 4 (ANGPTL4), were associated with more malignant clinical manifestations. Further, we discovered that extensive infiltration of B cells, dendritic cells, and mast cells indicated a favorable prognosis. CONCLUSIONS: The signature developed in this paper could be an effective model for estimating OS in lung cancer patients, and the immune cell infiltration analysis of the tumor immune microenvironment could shed light on more effective treatment in clinical practice.