ORIGINAL RESEARCH article
Front. Cell Dev. Biol.
Sec. Cellular Biochemistry
Volume 13 - 2025 | doi: 10.3389/fcell.2025.1619002
Bioinformatics-Based Screening and Validation of PANoptosis-Related Biomarkers in Periodontitis
Provisionally accepted
- Department of Endodontics and Periodontics, School of Stomatology, Dalian Medical University, Dalian, Liaoning, China
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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 coexpression 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.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).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.
Keywords: bioinformatics, biomarkers, Periodontitis, PANoptosis, ZBP1
Received: 27 Apr 2025; Accepted: 05 Jun 2025.
Copyright: © 2025 Sun, Hu, Wang, Guo, Zhang, Lu, Yang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xue Yang, Department of Endodontics and Periodontics, School of Stomatology, Dalian Medical University, Dalian, Liaoning, China
Lina Wang, Department of Endodontics and Periodontics, School of Stomatology, Dalian Medical University, Dalian, Liaoning, China
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