Development and validation of an immune‐related gene signature for prognosis in Lung adenocarcinoma. Issue 1 (2nd February 2023) - Guo, Zehuai; Qi, Xiangjun; Li, Zeyun; Yang, Jianying; Sun, Zhe; Li, Peiqin; Chen, Ming; Cao, Yang
Abstract: The most common type of lung cancer tissue is lung adenocarcinoma. The TCGA‐LUAD cohort retrieved from the TCGA dataset was considered the internal training cohort, while GSE68465 and GSE13213 datasets from the GEO database were used as the external test cohort. The TCGA‐LUAD cohort was classified into two immune subtypes using single‐sample gene set enrichment analysis of the immune gene set and unsupervised clustering analysis. The ESTIMATE algorithm, the CIBERSORT algorithm, and HLA family expression levels again validated the reliability of this typing. We performed Venn analysis using immune‐related genes from the immport dataset and differentially expressed genes from the subtypes to retrieve differentially expressed immune genes (DEIGs). In addition, DEIGs were used to construct a prognostic model with the least absolute shrinkage and selection operator regression analysis. A reliable risk model consisting of 11 DEIGs, including S100P, INHA, SEMA7A, INSL4, CD40LG, AGER, SERPIND1, CD1D, CX3CR1, SFTPD, and CD79A, was then built, and its reliability was further confirmed by ROC curve and calibration plot analysis. The high‐risk score subgroup had a poor prognosis and a lower tumour immune dysfunction and exclusion score, indicating a greater likelihood of anti‐PD‐1/cytotoxic T lymphocyte antigen 4 benefit. Abstract : We constructed a risk model that predicts both survival time and the efficacy of immunotherapy in patients with lung adenocarcinoma by taking the intersection of immunogenes and differential genes derived from immunophenotyping. The reliability of this model was validated with other datasets and the high‐ and low‐risk groups showed significant variability in several indicators.