Characterization of natural killer (NK) cells in lung adenocarcinoma and construction of an NK risk signature based on single-cell and macromolecular RNA-seg data

BACKGROUND/AIMS: Natural killer (NK) cells play a crucial role in tumor cell apoptosis, immune milieu regulation, and angiogenesis inhibition. This study aims to analyze the NK signature in lung adenocarcinoma (LUAD) and establish an NK cell-based risk signature for predicting the prognosis of LUAD patients. METHODS: Single-cell RNA sequencing (scRNA-seq) data were obtained from the GEO database, while RNA-seq and microarray data from LUAD were simultaneously obtained from the TCGA and GEO databases. The scRNA-seq data were processed using the Seurat R package to identify NK clusters based on NK markers. Differentially expressed genes (DEGs) between normal and tumor samples were identified through differential expression analysis of LUAD-related data. Pearson correlation analysis was used to identify DEGs associated with NK clusters, followed by one-way Cox regression analysis to identify NK cell-related prognostic genes. Subsequently, Lasso regression analysis was employed to construct a risk signature based on NK cell-related prognostic genes. Finally, a column-line diagram model was constructed based on the risk signature and clinicopathological features. RESULTS: Based on the scRNA-seq data, we identified five Natural killer (NK)cells clusters in lung adenocarcinoma (LUAD), with four of them showing associations with prognosis in LUAD. Out of 19,495 differentially expressed genes (DEGs), a total of 725 genes significantly associated with NK clusters were pinpointed and further narrowed down to form a risk profile comprising 13 genes. These 13 genes were primarily linked to 21 signaling pathways, including vascular smooth muscle contraction, RNA polymerase, and pyrimidine metabolism. Additionally, the risk profile exhibited significant associations with stromal and immune scores, as well as various immune cells. Multifactorial analysis indicated that the risk profile served as an independent prognostic factor for LUAD, and its efficacy in predicting the outcome of immunotherapy was validated. Furthermore, a novel column-line diagram integrating staging and NK-based risk profiles was developed, demonstrating strong predictability and reliability in prognostic forecasting for LUAD. CONCLUSION: The NK cell-based risk signature proves to be a valuable tool for predicting the prognosis of patients with LUAD. Furthermore, a comprehensive understanding of NK cell characterization in LUAD could potentially unveil insights into the response of LUAD to immunotherapies and offer novel strategies for cancer treatment.