Bioinformatics-based screening and validation of PANoptosis-related biomarkers in periodontitis

Qing Sun; JinYue Hu; RuYue Wang; ShuiXiang Guo; GeGe Zhang; Ao Lu; Xue Yang; LiNa Wang

doi:10.3389/fcell.2025.1619002

Bioinformatics-based screening and validation of PANoptosis-related biomarkers in periodontitis

Front Cell Dev Biol. 2025 Jun 19:13:1619002. doi: 10.3389/fcell.2025.1619002. eCollection 2025.

Authors

Qing Sun^#^{1

2}, JinYue Hu^#^{1

2}, RuYue Wang^{1

2}, ShuiXiang Guo^{1

3}, GeGe Zhang^{1

3}, Ao Lu^{1

3}, Xue Yang^{1

2}, LiNa Wang^{1

2}

Affiliations

¹ Department of Endodontics and Periodontics, School of Stomatology, Dalian Medical University, Dalian, Liaoning, China.
² The Affiliated Stomatological Hospital of Dalian Medical University, School of Stomatology, Dalian, Liaoning, China.
³ Academician Laboratory of Immune and Oral Development & Regeneration, Dalian Medical University, Dalian, Liaoning, China.

^# Contributed equally.

Abstract

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.

Keywords: PANoptosis; ZBP1; bioinformatics; biomarkers; periodontitis.