Wei Lulu, Chen Miaomiao, Shi Xin, Wang Yibing, Yang Shengwei
Department of Stomatology, Northwest University First Hospital, Xi'an, Shaanxi, China.
Xi'an International University, Xi'an, Shaanxi, China.
Sci Rep. 2025 Aug 16;15(1):30008. doi: 10.1038/s41598-025-15330-z.
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.
牙周炎是一种常见的炎症性疾病，会影响牙齿周围和支持牙齿的组织，如果不治疗，最终会导致牙齿脱落。本研究旨在探讨脂质代谢相关基因（LMRGs）的诊断潜力，并描绘牙周炎中的免疫微环境格局。差异表达分析确定了差异表达的LMRGs（DELMRGs），随后进行功能富集分析以阐明其生物学功能。使用随机森林、最小绝对收缩和选择算子（LASSO）回归以及XGBoost确定枢纽DELMRGs。使用受试者工作特征（ROC）曲线评估这些基因的诊断性能。分别使用ImmuCellAI和基因集变异分析（GSVA）分析免疫细胞浸润和免疫功能状态。采用单细胞RNA测序（scRNA-seq）在单细胞分辨率下解析牙周炎中的免疫微环境和细胞通讯网络。机器学习方法揭示了五个枢纽LMRGs：FABP4、CWH43、CLN8、ADGRF5和OSBPL6。ADGRF5和FABP4在牙周炎样本中显著上调，而CWH43、CLN8和OSBPL6下调。联合LMRGs评分表现出优异的诊断性能，曲线下面积（AUC）为0.954。免疫细胞浸润分析揭示了牙周炎中LMRGs评分与各种T细胞亚群之间存在显著正相关。GSVA表明牙周炎中抗原呈递过程和多种免疫相关途径被激活。scRNA-seq描绘了八种不同的细胞类型，关键LMRGs在不同细胞类型中差异表达。细胞通讯分析突出了由MHC-II、CXCL和ADGRE5信号通路介导的显著相互作用。单核细胞和多能祖细胞（MPP）主要促成炎症反应。对单核细胞异质性的进一步分析确定了五个具有不同作用的单核细胞簇，包括免疫和炎症反应激活以及与细胞增殖和代谢相关的途径。总之，反映脂质代谢作用的综合LMRGs评分是牙周炎一个有前景的诊断生物标志物。此外，详细的免疫细胞浸润和单细胞分析强调了免疫微环境在牙周炎发病机制中的关键作用。
