Unraveling the role of M1 macrophage and CXCL9 in predicting immune checkpoint inhibitor efficacy through multicohort analysis and single-cell RNA sequencing

The exact function of M1 macrophages and CXCL9 in forecasting the effectiveness of immune checkpoint inhibitors (ICIs) is still not thoroughly investigated. We investigated the potential of M1 macrophage and C-X-C Motif Chemokine Ligand 9 (CXCL9) as predictive markers for ICI efficacy, employing a comprehensive approach integrating multicohort analysis and single-cell RNA sequencing. A significant correlation between high M1 macrophage and improved overall survival (OS) and objective response rate (ORR) was found. M1 macrophage expression was most pronounced in the immune-inflamed phenotype, aligning with increased expression of immune checkpoints. Furthermore, CXCL9 was identified as a key marker gene that positively correlated with M1 macrophage and response to ICIs, while also exhibiting associations with immune-related pathways and immune cell infiltration. Additionally, through exploring RNA epigenetic modifications, we identified Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3G (APOBEC3G) as linked to ICI response, with high expression correlating with improved OS and immune-related pathways. Moreover, a novel model based on M1 macrophage, CXCL9, and APOBEC3G-related genes was developed using multi-level attention graph neural network, which showed promising predictive ability for ORR. This study illuminates the pivotal contributions of M1 macrophages and CXCL9 in shaping an immune-active microenvironment, correlating with enhanced ICI efficacy. The combination of M1 macrophage, CXCL9, and APOBEC3G provides a novel model for predicting clinical outcomes of ICI therapy, facilitating personalized immunotherapy. This study explores M1 macrophage and CXCL9 as predictive markers for immune checkpoint inhibitor (ICI) efficacy through multicohort analyses. High M1 macrophage correlates with improved ICI efficacy, and CXCL9 is identified as a key marker gene positively correlated with M1 macrophage and ICI efficacy. Transcriptomics and scRNA-seq analyses unveil APOBEC3G as a novel biomarker for predicting ICI response. A novel multi-level attention graph neural network model is proposed, showing promising performance in predicting ICI response. # image