Exploring the role of lipid metabolism related genes and immune microenvironment in periodontitis by integrating machine learning and bioinformatics analysis.

Periodontitis is a common inflammatory disease affecting the tissues surrounding and supporting the teeth, ultimately leading to tooth loss if left untreated. This study aimed to investigate the diagnostic potential of lipid metabolism-related genes (LMRGs) and characterize the immune microenvironment landscape in periodontitis. Differential expression analysis identified differentially expressed LMRGs (DELMRGs), followed by functional enrichment analyses to elucidate their biological functions. Hub DELMRGs were identified using Random Forest, least absolute shrinkage and selection operator (LASSO) regression, and XGBoost. The diagnostic performance of these genes was assessed using receiver operating characteristic (ROC) curves. Immune cell infiltration and immune function status were analyzed using ImmuCellAI and Gene Set Variation Analysis (GSVA), respectively. Single-cell RNA sequencing (scRNA-seq) was employed to decode the immune microenvironment and cell communication networks at single-cell resolution in periodontitis. Machine learning approaches revealed five hub LMRGs: FABP4, CWH43, CLN8, ADGRF5, and OSBPL6. ADGRF5 and FABP4 were significantly upregulated in periodontitis samples, while CWH43, CLN8, and OSBPL6 were downregulated. The combined LMRGs score exhibited excellent diagnostic performance with an area under the curve (AUC) of 0.954. Immune cell infiltration analysis unveiled significant positive correlations between LMRGs score and various T cell subsets in periodontitis. GSVA indicated activation of antigen presentation processes and multiple immune-related pathways in periodontitis. scRNA-seq delineated eight distinct cell types, with key LMRGs differentially expressed across cell types. Cell communication analysis highlighted significant interactions mediated by MHC-II, CXCL, and ADGRE5 signaling pathways. Monocytes and multipotent progenitor cells (MPPs) primarily contributed to the inflammatory response. Further analysis of monocyte heterogeneity identified five monocyte clusters with distinct roles, including immune and inflammatory response activation and pathways related to cell proliferation and metabolism.In summary, the integrated LMRGs score, which reflects lipid metabolism's role, represents a promising diagnostic biomarker for periodontitis. Additionally, detailed immune cell infiltration and single-cell analyses underscored the critical role of the immune microenvironment in periodontitis pathogenesis.