Sun Qing, Hu JinYue, Wang RuYue, Guo ShuiXiang, Zhang GeGe, Lu Ao, Yang Xue, Wang LiNa
Department of Endodontics and Periodontics, School of Stomatology, Dalian Medical University, Dalian, Liaoning, China.
The Affiliated Stomatological Hospital of Dalian Medical University, School of Stomatology, Dalian, Liaoning, China.
Front Cell Dev Biol. 2025 Jun 19;13:1619002. doi: 10.3389/fcell.2025.1619002. eCollection 2025.
BACKGROUND: Periodontitis is the most prevalent chronic inflammatory disease affecting the periodontal tissues. PANoptosis, a recently characterized form of programmed cell death, has been implicated in various pathological processes; however, its mechanistic role in periodontitis remains unclear. This study integrates multi-omics data and machine learning approaches to systematically identify and validate key PANoptosis-related biomarkers in periodontitis. METHODS: Periodontitis-related microarray datasets (GSE16134 and GSE10334) were obtained from the GEO database, and PANoptosis-related genes were retrieved from GeneCards. Differential gene expression analysis was performed using the GSE16134 dataset, followed by weighted gene co-expression network analysis (WGCNA) to identify relevant gene modules. The intersection of differentially expressed genes and WGCNA modules was used to define differentially expressed PANoptosis-related genes (PRGs). Protein-protein interaction (PPI) networks of these PRGs were constructed using the STRING database and visualized with Cytoscape. Subnetworks were identified using the MCODE plugin. Key genes were selected based on integration with rank-sum test results. Functional enrichment analysis was performed for these key genes. Machine learning algorithms were then applied to screen for potential biomarkers. Diagnostic performance was assessed using receiver operating characteristic (ROC) curves and box plots. The relationship between selected biomarkers and immune cell infiltration was explored using the CIBERSORT algorithm. Finally, RT-qPCR was conducted to validate biomarker expression in clinical gingival tissue samples. RESULTS: Through comprehensive bioinformatics analysis and literature review, ZBP1 was identified as a PANoptosis-related biomarker in periodontitis. RT-qPCR validation demonstrated that ZBP1 expression was significantly elevated in periodontitis tissues compared to healthy periodontal tissues (P < 0.05). CONCLUSION: This study provides bioinformatic evidence linking PANoptosis to periodontitis. ZBP1 was identified as a key PANoptosis-related biomarker, suggesting that periodontitis may involve activation of the ZBP1-mediated PANoptosome complex.
背景：牙周炎是影响牙周组织的最常见慢性炎症性疾病。PANoptosis是一种最近被表征的程序性细胞死亡形式，已涉及各种病理过程；然而，其在牙周炎中的机制作用仍不清楚。本研究整合多组学数据和机器学习方法，以系统地识别和验证牙周炎中与PANoptosis相关的关键生物标志物。 方法：从GEO数据库中获取牙周炎相关的微阵列数据集（GSE16134和GSE10334），并从GeneCards中检索与PANoptosis相关的基因。使用GSE16134数据集进行差异基因表达分析，随后进行加权基因共表达网络分析（WGCNA）以识别相关基因模块。差异表达基因与WGCNA模块的交集用于定义差异表达的与PANoptosis相关的基因（PRGs）。使用STRING数据库构建这些PRGs的蛋白质-蛋白质相互作用（PPI）网络，并用Cytoscape进行可视化。使用MCODE插件识别子网。基于与秩和检验结果的整合选择关键基因。对这些关键基因进行功能富集分析。然后应用机器学习算法筛选潜在的生物标志物。使用受试者工作特征（ROC）曲线和箱线图评估诊断性能。使用CIBERSORT算法探索所选生物标志物与免疫细胞浸润之间的关系。最后，进行RT-qPCR以验证临床牙龈组织样本中生物标志物的表达。 结果：通过全面的生物信息学分析和文献综述，ZBP1被鉴定为牙周炎中与PANoptosis相关的生物标志物。RT-qPCR验证表明，与健康牙周组织相比，牙周炎组织中ZBP1表达显著升高（P < 0.05）。 结论：本研究提供了将PANoptosis与牙周炎联系起来的生物信息学证据。ZBP1被鉴定为关键的与PANoptosis相关的生物标志物，表明牙周炎可能涉及ZBP1介导的PANoptosome复合物的激活。
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