Identification of Periodontal Disease Diagnostic Markers Via Data Cross-Validation

IF 3.7 3区 医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE International dental journal Pub Date : 2025-06-01 Epub Date: 2025-02-04 DOI:10.1016/j.identj.2025.01.011
Juan Du, Yi Liu, Zhenhua Luo, Minfeng Wang, Yitong Liu
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Abstract

Introduction and aims

Periodontitis is a globally prevalent disease that is clinically diagnosed when the periodontal tissues are pathologically affected. Therefore, it is vital to identify novel periodontitis-associated biomarkers that will aid in diagnosing or treating potential patients with periodontitis.

Methods

The GSE16134 and GSE10334 datasets were downloaded from the Gene Expression Omnibus database to identify differentially expressed genes between periodontitis and healthy samples. Single-sample gene set enrichment analysis was performed to identify significantly involved signalling pathways. Weighted gene correlation network analysis was used to identify key molecular modules. Hub genes were screened using key genes to construct a diagnosis and prediction model of periodontitis. Microenvironment cell population-counter was used to analyse immune cell infiltration patterns in periodontal diseases.

Results

Single-sample gene set enrichment analysis revealed that periodontitis involves the PI3K/AKT/mTOR signalling pathway and associated module genes (667 genes). Kyoto Encyclopedia of Genes and Genomes enrichment analysis of the module genes revealed that periodontitis involves the type I interferon, rhythmic process, and response to type I interferon signalling pathways. GSEA identified 21 core genes associated with periodontitis and classified them into two clusters, A and B. Genomics of Drug Sensitivity in Cancer analysis revealed that AKT.inhibitor.VIII had high drug sensitivity in the cluster A subtype. Monocytes and myeloid dendritic cell infiltration were enriched in the cluster A subtype, whereas natural killer T cell infiltration was enriched in the cluster B subtype.

Conclusion

The pathway and gene modules identified in this study may help comprehensively diagnose periodontitis and provide a novel method for evaluating new treatments.

Clinical relevance

Our results are beneficial for classifying periodontitis subtypes and treatment using targeted medicine.
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通过数据交叉验证鉴定牙周病诊断标志物。
简介和目的:牙周炎是一种全球流行的疾病,当牙周组织受到病理影响时,临床诊断为牙周炎。因此,确定新的与牙周炎相关的生物标志物将有助于诊断或治疗潜在的牙周炎患者是至关重要的。方法:从Gene Expression Omnibus数据库下载GSE16134和GSE10334数据集,鉴定牙周炎样本与健康样本之间的差异表达基因。进行单样本基因集富集分析,以确定显著参与的信号通路。采用加权基因相关网络分析方法鉴定关键分子模块。利用关键基因筛选枢纽基因,构建牙周炎的诊断预测模型。采用微环境细胞群计数器分析牙周病患者免疫细胞浸润模式。结果:单样本基因集富集分析显示牙周炎涉及PI3K/AKT/mTOR信号通路及其相关模块基因(667个基因)。京都基因百科和基因组模块基因的富集分析显示,牙周炎涉及I型干扰素,节律过程和对I型干扰素信号通路的反应。GSEA鉴定出21个与牙周炎相关的核心基因,并将其分为A和b两类。肿瘤药物敏感性基因组学分析显示,AKT.inhibitor.VIII在A类亚型中具有较高的药物敏感性。单核细胞和髓样树突状细胞浸润在集群A亚型中富集,而自然杀伤T细胞浸润在集群B亚型中富集。结论:本研究确定的途径和基因模块有助于牙周炎的全面诊断,并为评估新的治疗方法提供新的方法。临床意义:本研究结果有助于牙周炎亚型的分类和靶向药物的治疗。
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来源期刊
International dental journal
International dental journal 医学-牙科与口腔外科
CiteScore
4.80
自引率
6.10%
发文量
159
审稿时长
63 days
期刊介绍: The International Dental Journal features peer-reviewed, scientific articles relevant to international oral health issues, as well as practical, informative articles aimed at clinicians.
期刊最新文献
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