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ORIGINAL RESEARCH article

Front. Dent. Med
Sec. Periodontics
Volume 5 - 2024 | doi: 10.3389/fdmed.2024.1480346

Genetic analysis of potential markers and therapeutic targets for immunity in periodontitis

Provisionally accepted
Hui Li Hui Li 1Wanqing du Wanqing du 1Xin Ye Xin Ye 2Xi Luo Xi Luo 2Xuejing Duan Xuejing Duan 2*
  • 1 School of Stomatology, Shandong First Medical University, Jinan, China
  • 2 Department of Stomatology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China

The final, formatted version of the article will be published soon.

    Objective: Periodontitis is a chronic inflammatory periodontal disease resulting in destroyed periodontal tissue. Many studies have found that the host's inflammatory immune responses are involved in the risk of periodontal tissue damage. In this study, we aim to identify potential biomarkers and therapeutic targets related to immunity in periodontitis.Methods: GSE16134 and GSE10334 were downloaded from the Gene Expression Omnibus (GEO) database, and the immune-related genes were obtained from the Immunology Database and Analysis Portal (ImmPort). After the differentially expressed immune-related genes (DE-IRGs) were identified, enrichment analysis was performed. Two machine learning methods, the least absolute shrinkage and selector operation (LASSO) logistic regression and the support vector machine-recursive feature elimination (SVM-RFE), were used to screen out potential markers for the diagnosis of periodontitis. The CIBERSORT algorithm and LM22 matrix were used to analyze the percentage of infiltrating immune cells in periodontitis. Finally, the potential drug targets for the selected immune-related marker genes were predicted using relevant databases.Results: A total of 7 genes (CD19, CXCR4, FABP4, FOS, IGHD, IL2RG, and PPBP) were upregulated in periodontitis samples. The area under the receiver operating characteristic curve (AUC) value of only one gene for distinguishing periodontitis from healthy samples ranged from 0.724 to 0.894. The prediction ability of the combined risk score of these 7 DE-IRGs was improved (AUC = 0.955). Naïve B cells, neutrophils, plasma cells, and activated memory CD4 T cells were significantly enriched in periodontitis samples, and 25 drugs targeting 4 DE-IRGs were predicted.We developed a diagnostic model based on seven IRGs for periodontitis. The possible drugs targeting IRGs may provide new ideas for periodontitis treatment.

    Keywords: Periodontitis, immune-related genes, Diagnostic model, Marker genes, Immunity

    Received: 20 Aug 2024; Accepted: 07 Nov 2024.

    Copyright: © 2024 Li, du, Ye, Luo and Duan. 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: Xuejing Duan, Department of Stomatology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.