Identification of an immune-related genessignature in lung adenocarcinoma to predict survival andresponse to immune checkpoint inhibitors

Background: Although advances in immune checkpoint inhibitor(ICI) research have provided a new treatment approachfor lung adenocarcinoma (LUAD) patients, their survival is stillunsatisfactory, and there are issues in the era of response predictionto immunotherapy.Materials and Methods: Using bioinformatics methods, aprognostic signature was constructed and its predictive abilitywas validated both in the internal and external datasets(GSE۶۸۴۶۵). We also explored the tumor-infiltrating immunecells, mutation profiles, and immunophenoscore (IPS) in thelow-and high-risk groups.Results: As far as we are aware, this is the first study whichintroduces a novel prognostic signature model using BIRC۵,CBLC, S۱۰۰P, SHC۳, ANOS۱, VIPR۱, LGR۴, PGC, and IGKV۴.۱. According to multivariate analysis, the ۹-immunerelatedgenes (IRGs) signature provided an independent prognosticfactor for the overall survival (OS). The low-risk grouphad better OS, and the tumor mutation burden (TMB) was significantlylower in this group. Moreover, the risk scores werenegatively associated with the tumor-infiltrating immune cells,like CD۸+ T cells, macrophages, dendritic cells, and NK cells.In addition, the IPS were significantly higher in the low-riskgroup as they had higher gene expression of immune checkpoints,suggesting that ICIs could be a promising treatment optionfor low-risk LUAD patients.Conclusion: The combination of these ۹-IRGs not only couldefficiently predict overall survival of LUAD patients but alsoshow a powerful association with the expression of immunecheckpoints and response to ICIs based on IPS; hoping thismodel paves the way for better stratification and managementof patients in clinical practice