Bioinformatics-based screening and validation of PANoptosis-related biomarkers in periodontitis Article Swipe
Qing Sun
,
Jingjing Hu
,
Rongsheng Wang
,
Sun‐Wei Guo
,
GeGe Zhang
,
Lu Ao
,
Yang Xue
,
Lina Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fcell.2025.1619002
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fcell.2025.1619002
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fcell.2025.1619002
- https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1619002/pdf
- OA Status
- gold
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411462963
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411462963Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fcell.2025.1619002Digital Object Identifier
- Title
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Bioinformatics-based screening and validation of PANoptosis-related biomarkers in periodontitisWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-19Full publication date if available
- Authors
-
Qing Sun, Jingjing Hu, Rongsheng Wang, Sun‐Wei Guo, GeGe Zhang, Lu Ao, Yang Xue, Lina WangList of authors in order
- Landing page
-
https://doi.org/10.3389/fcell.2025.1619002Publisher landing page
- PDF URL
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https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1619002/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1619002/pdfDirect OA link when available
- Concepts
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Periodontitis, Computational biology, Bioinformatics, Medicine, Biomarker, Biology, Internal medicine, GeneticsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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