Identification of key macrophage subtypes and therapeutic targets in periodontitis using integrated bioinformatics analysis and machine learning

Abstract The innate immune system is fundamental to the body's primary defence against pathogens and plays a critical role in the pathogenesis of periodontitis (PD). Macrophages, as a key component of this innate response, are instrumental in the disease's development and progression. However, our understanding of the distinct macrophage subtypes implicated in PD and the identification of novel therapeutic targets remains limited. We obtained single-cell RNA sequencing (scRNA-seq) and bulk sequencing data from PD patients from the Gene Expression Omnibus (GEO). By integrating the scRNA-seq data, we have constructed a comprehensive single-cell atlas of the human gingiva. Key macrophage subtypes were screened using gene set variation analysis (GSVA). CellChat was used to assess intercellular communication. Using three machine learning algorithms, four hub genes (FCN1, CD93, XBP1, CTSH) associated with FCN1 + MAC were identified. These results were further validated experimentally. We revealed that macrophage subtypes were closely associated with inflammation and bone destruction in PD patients. Four hub genes that could be used as biomarkers to distinguish PD were identified. Our study found novel disease markers and potential drug targets, providing a foundation for more precise and effective interventions in the management of PD.