Immunogenic cell death-related classification predicts prognosis and response to immunotherapy in NSCLC

<bold>Background:</bold> Immunogenic cell death (ICD) is a type of regulated cell death (RCD) which elicits adaptive immunity. A better understanding of ICD might contribute to modify the current regimens of anti-cancer therapy, especially immunotherapy. The aim of this study was to identify ICD-related gene signatures and investigate the effects of these signatures on predicting survival prognosis and immunotherapy in patients with NSCLS. <bold>Methods:</bold> RNA sequencing and clinical data were gathered from TCGA-LUADLUSC cohorts and GEO database. Differentially expressed genes were identified by comparing gene expression between tumors and normal tissue. LASSO-Cox analysis was utilized to identify hub genes and construct the prognostic signatures. GSEA and GSVA were used to explore the differentially expressed signaling pathway. Moreover, we identify genomic alterations and differences in biological processes, analyze the immune microenvironment, and predict the response to immunotherapy. The gene signature was further validated using independent datasets from GEO. <bold>Results:</bold> Seven genes (DSG2, AURKA, ITGB4, LDHA, IL33, TLR2, KPNA2) were individually associated with OS in univariate analyses. A risk score was constructed for each patient based on the coefficients in multivariate Cox model. Patients with a low risk score had a significantly better survival and efficacy of immune checkpoint inhibitor (ICI) treatment than those with a high risk score. Further analysis showed that heightened levels of immune cell infiltration expression are observed in the low risk score group. <bold>Conclusions:</bold> The ICD-related signatures we constructed can predict the prognosis of patients and identify ICI therapy.