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Background and objective Published studies proved that both pyroptosis and periodontitis owned a substantial relationship with immunity, and recent research revealed a solid correlation between periodontitis and pyroptosis. While abundant findings have confirmed pyroptosis has a strong impact on the tumor microenvironment, the function of pyroptosis in influencing the periodontitis immune microenvironment remains poorly understood. Thus, we aimed to identify pyroptosis‐related genes whose expression signature can well discriminate periodontitis from healthy controls and to comprehend the role of pyroptosis in the periodontitis immune microenvironment. Materials and methods The periodontitis‐related datasets were acquired from the Gene Expression Omnibus (GEO) database. A series of bioinformatics analyses were conducted to investigate the underlying mechanism of pyroptosis in the periodontitis immune microenvironment. Infiltrating immunocytes, immunological reaction gene sets, and the human leukocyte antigen (HLA) gene were all investigated as potential linkages between periodontitis immune microenvironment and pyroptosis. Results Twenty‐one pyroptosis‐related genes were dysregulated. A four‐mRNA combined classification model was constructed, and the receiver operating characteristic (ROC) curve analysis demonstrated its prominent classification capabilities. Subsequently, the mRNA levels of the four hub markers (CYCS, CASP3, NOD2, CHMP4B) were validated by quantitative real‐time PCR (qRT‐PCR). The correlation coefficients between each hub gene and immune characteristics were calculated, and CASP3 exhibited the most significant correlations with the immune characteristics. Furthermore, distinct pyroptosis‐related expression patterns were revealed, along with immunological features of each pattern. Afterward, we discovered 1868 pyroptosis phenotype‐related genes, 134 of which were related to immunity. According to the functional enrichment analysis, these 134 genes were closely related to cytokine signaling in immune system, and defense response. Finally, a co‐expression network was constructed via the 1868 gene expression profiles. Conclusion Four hub mRNAs (CYCS, CASP3, NOD2, and CHMP4B) formed a classification model and concomitant results revealed the crucial role of pyroptosis in the periodontitis immune microenvironment, providing fresh insights into the etiopathogenesis of periodontitis and potential immunotherapy.

Background and ObjectivesPeriodontitis, a prevalent chronic inflammatory condition, poses a significant risk of tooth loosening and subsequent tooth loss. Within the realm of programmed cell death, a recently recognized process known as necroptosis has garnered attention for its involvement in numerous inflammatory diseases. Nevertheless, its correlation with periodontitis is indistinct. Our study aimed to identify necroptosis‐related lncRNAs and crucial lncRNA‐miRNA‐mRNA regulatory axes in periodontitis to further understand the pathogenesis of periodontitis.Materials and MethodsGene expression profiles in gingival tissues were acquired from the Gene Expression Omnibus (GEO) database. Selecting hub necroptosis‐related lncRNA and extracting the key lncRNA‐miRNA‐mRNA axes based on the ceRNA network by adding novel machine‐learning models based on conventional analysis and combining qRT‐PCR validation. Then, an artificial neural network (ANN) model was constructed for lncRNA in regulatory axes, and the accuracy of the model was validated by receiver operating characteristic (ROC) curve analysis. The clinical effect of the model was evaluated by decision curve analysis (DCA). Weighted correlation network analysis (WGCNA) and single‐sample gene set enrichment analysis (ssGSEA) was performed to explore how these lncRNAs work in periodontitis.ResultsSeven hub necroptosis‐related lncRNAs and three lncRNA‐miRNA‐mRNA regulatory axes (RP11‐138A9.1/hsa‐miR‐98‐5p/ZBP1 axis, RP11‐96D1.11/hsa‐miR‐185‐5p/EZH2 axis, and RP4‐773 N10.4/hsa‐miR‐21‐5p/TLR3 axis) were predicted. WGCNA revealed that RP11‐138A9.1 was significantly correlated with the “purple module”. Functional enrichment analysis and ssGSEA demonstrated that the RP11‐138A9.1/hsa‐miR‐98‐5p/ZBP1 axis is closely related to the inflammation and immune processes in periodontitis.ConclusionOur study predicted a crucial necroptosis‐related regulatory axis (RP11‐138A9.1/hsa‐miR‐98‐5p/ZBP1) based on the ceRNA network, which may aid in elucidating the role and mechanism of necroptosis in periodontitis.