A novel classification method for LUAD that guides personalized immunotherapy on the basis of the cross-talk of coagulation- and macrophage-related genes

Purpose The coagulation process and infiltration of macrophages affect the progression and prognosis of lung adenocarcinoma (LUAD) patients. This study was designed to explore novel classification methods that better guide the precise treatment of LUAD patients on the basis of coagulation and macrophages. Methods Weighted gene coexpression network analysis (WGCNA) was applied to identify M2 macrophage-related genes, and TAM marker genes were acquired through the analysis of scRNA-seq data. The MSigDB and KEGG databases were used to obtain coagulation-associated genes. The intersecting genes were defined as coagulation and macrophage-related (COMAR) genes. Unsupervised clustering analysis was used to evaluate distinct COMAR patterns for LUAD patients on the basis of the COMAR genes. The R package “limma” was used to identify differentially expressed genes (DEGs) between COMAR patterns. A prognostic risk score model, which was validated through external data cohorts and clinical samples, was constructed on the basis of the COMAR DEGs. Results In total, 33 COMAR genes were obtained, and three COMAR LUAD subtypes were identified on the basis of the 33 COMAR genes. There were 341 DEGs identified between the three COMAR subtypes, and 60 prognostic genes were selected for constructing the COMAR risk score model. Finally, 15 prognosis-associated genes (CORO1A, EPHA4, FOXM1, HLF, IFIH1, KYNU, LY6D, MUC16, PPARG, S100A8, SPINK1, SPINK5, SPP1, VSIG4, and XIST) were included in the model, which was efficient and robust in predicting LUAD patient prognosis and clinical outcomes in patients receiving anti-PD-1/PD-L1 immunotherapy. Conclusions LUAD can be classified into three subtypes according to COMAR genes, which may provide guidance for precise treatment.