ORIGINAL RESEARCH article
Front. Med.
Sec. Translational Medicine
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1640961
This article is part of the Research TopicThe Application of Multi-omics Analysis in Translational MedicineView all 12 articles
Multi-Omics Integration to Identify Immune-Associated Biomarkers and Potential Therapeutics in Periodontitis
Provisionally accepted
- 1The First Outpatient Department, School and Hospital of Stomatology, Jilin University, Changchun, China
- 2Wuxi Stomatology Hospital, Wuxi, China
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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 1641 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. Conclusions: 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.
Keywords: Periodontitis, immune microenvironment, DNA Methylation, Machinelearning, diagnostic biomarkers
Received: 04 Jun 2025; Accepted: 22 Sep 2025.
Copyright: © 2025 Jin, Yuan and Liu. 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: Yin Liu, 17751495685@163.com
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