ArticleFrontiers in medicine2025
Multi-omics integration to identify immune-associated biomarkers and potential therapeutics in periodontitis.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Periodontology: A Systematic Review.Journal of periodontal research · 2026Pooled it
- Evolution of scaffold materials for periodontal therapeutics over two decades (2005-2025): A bibliometric mapping analysis.The Saudi dental journal · 2026Review
- CD48 and CXCR4 as immune-associated biomarkers in periodontitis: an integrative analysis and validation study.Frontiers in immunology · 2026Article
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Authors and funding
3 authors.
Funding
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Abstract
Background: Periodontitis, a chronic inflammatory disease of periodontal tissues, is linked to immune response and epigenetic modifications, with DNA methylation playing a crucial role. This study integrates transcriptomic and DNA methylation profiles from periodontitis patients to explore the immune microenvironment and identify potential biomarkers and therapeutic targets. Methods: Transcriptomic and methylation profiles from 24 periodontitis patients were analyzed to evaluate the immune microenvironment and identify related abnormal genes. WGCNA was used to identify immune cell-associated genes. Subsequently, machine learning algorithms identified diagnostic biomarkers for periodontitis, which then validated in two cohorts with 247 and 310 periodontitis patients, respectively. Finally, network pharmacology analysis identified potential targeted drugs for the candidate genes. Results: We obtained 23,528 differentially methylated sites and 1,641 differential expressed genes. Immune cell analysis identified eight abnormal cell types in periodontitis, and WGCNA highlighted two gene modules linked to these immune alterations. Machine learning with random forest and SVM identified nine key genes (ATP2C2, FAM43B, FOXA3, HSPA12A, KIF1C, NCS1, PGM1, RASSF6, SH2B2) with diagnostic efficacy, achieving high AUC scores across validation datasets. Network pharmacology analysis identified three drugs-bisphenol A, acetaminophen, and valproic acid-as potential regulators of these genes, offering new treatment avenues. Conclusion: Through integrating s transcriptomic and DNA methylation profiles, nine genes have been filtered as potential diagnostic biomarkers of periodontitis. Drugs targeting these genes may serve as potential therapeutics for periodontitis. These findings reveal valuable insights into immune and epigenetic mechanisms in periodontitis, presenting new biomarkers and therapeutic options that may enhance clinical diagnosis and treatment of the disease and provide unique insights for further exploration of the pathogenesis of periodontitis and the development of related therapeutic drugs.
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