No abstract

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.

BackgroundPeriodontitis (PD), an age-related disease, is characterized by inflammatory periodontal tissue loss, and with the general aging of the global population, the burden of PD is becoming a major health concern. Nevertheless, the mechanism underlying this phenomenon remains indistinct. We aimed to develop a classification model for PD and explore the relationship between aging subtypes and the immune microenvironment for PD based on bioinformatics analysis.Materials and MethodsThe PD-related datasets were acquired from the Gene Expression Omnibus (GEO) database, and aging-related genes (ARGs) were obtained from the Human Aging Genomic Resources (HAGR). Four machine learning algorithms were applied to screen out the hub ARGs. Then, an artificial neural network (ANN) model was constructed 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). Consensus clustering was employed to determine the aging expression subtypes. A series of bioinformatics analyses were performed to explore the PD immune microenvironment and its subtypes. The hub aging-related modules were defined using weighted correlation network analysis (WGCNA).ResultsTwenty-seven differentially expressed ARGs were dysregulated and a classifier based on four hub ARGs (BLM, FOS, IGFBP3, and PDGFRB) was constructed to diagnose PD with excellent accuracy. Subsequently, the mRNA levels of the hub ARGs were validated by quantitative real-time PCR (qRT-PCR). Based on differentially expressed ARGs, two aging-related subtypes were identified. Distinct biological functions and immune characteristics including infiltrating immunocytes, immunological reaction gene sets, the human leukocyte antigen (HLA) gene, and immune checkpoints were revealed between the subtypes. Additionally, the black module correlated with subtype-1 was manifested as the hub aging-related module and its latent functions were identified.ConclusionOur findings highlight the critical implications of aging-related genes in modulating the immune microenvironment. Four hub ARGs (BLM, FOS, IGFBP3, and PDGFRB) formed a classification model, and accompanied findings revealed the essential role of aging in the immune microenvironment for PD, providing fresh inspiration for PD etiopathogenesis and potential immunotherapy.