Identification of immune activation-related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma

Abstract

Background Lung adenocarcinoma (LUAD) is a major subtype of non-small cell lung cancer (NSCLC) with a highly heterogeneous tumor microenvironment. Immune checkpoint inhibitors (ICIs) are more effective in tumors with a pre-activated immune status. However, the potential of the immune activation-associated gene (IAG) signature for prognosis prediction and immunotherapy response assessment in LUAD has not been established. Therefore, it is critical to explore such gene signatures. Methods RNA sequencing profiles and corresponding clinical parameters of LUAD were extracted from the TCGA and GEO databases. Unsupervised consistency clustering analysis based on immune activation-related genes was performed on the enrolled samples. Subsequently, prognostic models based on genes associated with prognosis were built using the last absolute shrinkage and selection operator (LASSO) method and univariate Cox regression. The expression levels of four immune activation related gene index (IARGI) related genes were validated in 12 pairs of LUAD tumor and normal tissue samples using qPCR. Using the ESTIMATE, TIMER, and ssGSEA algorithms, immune cell infiltration analysis was carried out for different groups, and the tumor immune dysfunction and rejection (TIDE) score was used to evaluate the effectiveness of immunotherapy. Results Based on the expression patterns of IAGs, the TCGA LUAD cohort was classified into two clusters, with those in the IAG-high pattern demonstrating significantly better survival outcomes and immune cell infiltration compared to those in the IAG-low pattern. Then, we developed an IARGI model that effectively stratified patients into different risk groups, revealing differences in prognosis, mutation profiles, and immune cell infiltration within the tumor microenvironment between the high and low-risk groups. Notably, significant disparities in TIDE score between the two groups suggest that the low-risk group may exhibit better responses to ICIs therapy. The IARGI risk model was validated across multiple datasets and demonstrated exceptional performance in predicting overall survival in LUAD, and an IARGI-integrated nomogram was established as a quantitative tool for clinical practice. Conclusion The IARGI can serve as valuable biomarkers for evaluating the tumor microenvironment and predicting the prognosis of LUAD patients. Furthermore, these genes probably provide valuable guidance for establishing effective immunotherapy regimens for LUAD patients.