Development and validation of an immune-related gene set (IRGS) model for prognostic and immunotherapeutic assessment in lung adenocarcinoma (LUAD).

Background: With the rapid development of immunotherapy for solid tumors, the exploration of immune characteristics is becoming more and more important. Although single biomarkers such as programmed cell death-ligand 1 (PD-L1) expression and tumor mutational burden (TMB) are clinically used, their predictive value is imperfect across molecular subgroups and clinical settings, more exploration of immune characteristics will bring more opportunities for clinical diagnosis and treatment. Given the high morbidity and mortality of lung adenocarcinoma (LUAD) in the Chinese population, the aim of this study is to explore the immune characteristics of LUAD patients and lay the foundation for better prognosis and immunotherapy. Methods: Eight Gene Expression Omnibus (GEO) cohorts were used to identify immune and prognostically relevant genes. An immune-related gene set (IRGS) score predictive model was constructed using the single-sample gene set enrichment analysis (ssGSEA) algorithm and internally validated. The performance of the model was further verified in five external validation cohorts. To evaluate immune cell infiltration, Tumor Immune Estimation Resource (TIMER), XCELL, which digitally portrays the tissue cellular heterogeneity landscape, and Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) were applied to quantify the relative proportions of the infiltrating immune cells. Results: The patients with high IRGS scores had significantly better overall survival (OS) than those with low IRGS scores [hazard ratio (HR) =0.56; 95% confidence interval (CI): 0.46-0.68; P<0.001] in the training set. Similar results were obtained in the validation set (HR =0.45; 95% CI: 0.33-0.6; P<0.001). Further validation in five external cohorts yielded consistent results [GSE31210: P<0.001; GSE68465: P=0.04; Chen_2019: P=0.03; The Cancer Genome Atlas (TCGA)_LUAD: P=0.002; Clinical Proteomic Tumor Analysis Consortium (CPTAC)_LUAD: P=0.04]. In the tumor microenvironment analysis, the patients with high IRGS scores had higher levels of T cells, B cells, dendritic cells (DCs), and neutrophils. The immunotherapy analysis of a public cohort showed that the patients with high IRGS scores had better progression-free survival (PFS) after immunotherapy than those with low IRGS scores (P=0.01). Conclusions: Patients with high IRGS scores had a better prognosis and improved immune efficacy. The IRGS score model appears to have a good predictive performance, indicating its potential value in clinical applications.