Identification of an immune-related genes signature in lung adenocarcinoma to predict survival and response to immune checkpoint inhibitors

Abstract Background Although advances in immune checkpoint inhibitor (ICI) research have provided a new treatment approach for lung adenocarcinoma (LUAD) patients, their survival is still unsatisfactory, and there are issues in the era of response prediction to immunotherapy. We aimed to develop a prognostic model based on immune-related genes (IRGs) to predict the overall survival (OS) as well as response to ICIs in LUAD patients. Methods Using bioinformatics methods, a prognostic signature was constructed and its predictive ability was validated both in the internal and external datasets (GSE68465). We also explored the tumor-infiltrating immune cells, mutation profiles, and immunophenoscore (IPS) in the low-and high-risk groups. Results A prognostic signature based on 9-IRGs, including BIRC5, CBLC, S100P, SHC3, ANOS1, VIPR1, LGR4, PGC, and IGKV4.1 was developed. According to multivariate analysis, the 9-IRG signature provided an independent prognostic factor for OS in LUAD patients. The low-risk group had better OS, and the tumor mutation burden (TMB) was significantly lower in this group. Moreover, the risk scores were negatively associated with the tumor-infiltrating immune cells, like CD8+ T cells, macrophages, dendritic cells, and NK cells. In addition, the IPS were significantly higher in the low-risk group as they had higher gene expression of immune checkpoints, suggesting that ICIs could be a promising treatment option for low-risk LUAD patients. Conclusion Our 9-IRGs prognostic signature could be useful in predicting the survival of LUAD patients and their response to ICIs; hoping this model paves the way for better stratification and management of patients in clinical practice.