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Abstract
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 expressionanalysis 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.
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