Wu Qibing, Niu Yixi, Li Hanmo, Pan Yaping, Li Chen
Department of Periodontology, School and Hospital of Stomatology, China Medical University, No.117 Nanjing North Street, Heping District, Shenyang, 110002, Liaoning, China.
Liaoning Provincial Key Laboratory of Oral Diseases, Shenyang, China.
Inflammation. 2025 Aug;48(4):2087-2104. doi: 10.1007/s10753-024-02177-1. Epub 2024 Nov 29.
Periodontitis is a chronic inflammatory disease strongly influenced by host's immune response. Aberrant sialylation on cell surface plays a key role in inflammation and immunity. This study aims to identify sialylation-related genes associated with periodontitis and explore their impact on periodontal immune microenvironment. Differential expression analysis and machine learning were employed to determine core sialylation-related genes after datasets were retrieved and integrated. A diagnostic model incorporating these genes was constructed, following the immune cell infiltration analysis. Consensus clustering and weighted gene co-expression network analysis stratified periodontitis patients into subgroups and identified associated module genes. Single-cell sequencing data was further utilized to investigate the impact of sialylation on the periodontal immune microenvironment with pseudo-time series analysis and cell communication analysis. Periodontitis had a higher sialylation score with six key sialylation genes (CHST2, SELP, ST6GAL1, ST3GAL1, NEU1, FCN1) identified. The multi-gene diagnostic model demonstrated high accuracy and efficacy. Significant associations were observed between the key genes and immune cell populations, such as monocytes and B cells, in the periodontal immune microenvironment. Clustering analysis revealed two distinct sialylation-related subgroups with differential immune profiles. Single-cell data showed a significantly higher expression of sialylation-related genes in monocytes, which was found to significantly impact their developmental processes as well as their intercellular communication with B cells. The six identified sialylation-related genes hold potential as periodontitis biomarkers. High sialylation expression can impact the differentiation and cell-cell communication of monocytes. Sialylation-related genes are closely associated with alterations in the periodontal immune microenvironment.
牙周炎是一种受宿主免疫反应强烈影响的慢性炎症性疾病。细胞表面异常的唾液酸化在炎症和免疫中起关键作用。本研究旨在鉴定与牙周炎相关的唾液酸化相关基因,并探讨它们对牙周免疫微环境的影响。在检索和整合数据集后,采用差异表达分析和机器学习来确定核心唾液酸化相关基因。在进行免疫细胞浸润分析后,构建了包含这些基因的诊断模型。共识聚类和加权基因共表达网络分析将牙周炎患者分层为亚组,并鉴定了相关的模块基因。利用单细胞测序数据,通过伪时间序列分析和细胞通讯分析,进一步研究唾液酸化对牙周免疫微环境的影响。牙周炎的唾液酸化评分较高,鉴定出六个关键的唾液酸化基因(CHST2、SELP、ST6GAL1、ST3GAL1、NEU1、FCN1)。多基因诊断模型显示出高准确性和有效性。在牙周免疫微环境中,关键基因与免疫细胞群体(如单核细胞和B细胞)之间观察到显著关联。聚类分析揭示了两个具有不同免疫特征的明显的唾液酸化相关亚组。单细胞数据显示单核细胞中唾液酸化相关基因的表达显著更高,发现这对其发育过程以及与B细胞的细胞间通讯有显著影响。鉴定出的六个唾液酸化相关基因具有作为牙周炎生物标志物的潜力。高唾液酸化表达可影响单核细胞的分化和细胞间通讯。唾液酸化相关基因与牙周免疫微环境的改变密切相关。