Department of Stomatology, Sunshine Union Hospital, Yingqian Road, High-tech Zone, Weifang, 261000, Shandong, China.
BMC Oral Health. 2023 Mar 9;23(1):135. doi: 10.1186/s12903-023-02822-5.
The aim of this study was to reveal the biological function of endoplasmic reticulum stress (ERS)-related genes (ERSGs) in periodontitis, and provide potential ERS diagnostic markers for clinical therapy of periodontitis.
The differentially expressed ERSGs (DE-ERSGs) were reveled based on periodontitis-related microarray dataset in Gene Expression Omnibus (GEO) database and 295 ERS in previous study, followed by a protein-protein interaction network construction. Then, the subtypes of periodontitis were explored, followed by validation with immune cell infiltration and gene set enrichment. Two machine learning algorithms were used to reveal potential ERS diagnostic markers of periodontitis. The diagnostic effect, target drug and immune correlation of these markers were further evaluated. Finally, a microRNA(miRNA)-gene interaction network was constructed.
A total of 34 DE-ERSGs were revealed between periodontitis samples and control, followed by two subtypes investigated. There was a significant difference of ERS score, immune infiltration and Hallmark enrichment between two subtypes. Then, totally 7 ERS diagnostic markers including FCGR2B, XBP1, EDEM2, ATP2A3, ERLEC1, HYOU1 and YOD1 were explored, and the v the time-dependent ROC analysis showed a reliable result. In addition, a drug-gene network was constructed with 4 up-regulated ERS diagnostic markers and 24 drugs. Finally, based on 32 interactions, 5 diagnostic markers and 20 miRNAs, a miRNA-target network was constructed.
Up-regulated miR-671-5p might take part in the progression of periodontitis via stimulating the expression of ATP2A3. ERSGs including XBP1 and FCGR2B might be novel diagnostic marker for periodontitis.
本研究旨在揭示内质网应激（ERS）相关基因（ERSGs）在牙周炎中的生物学功能，并为牙周炎的临床治疗提供潜在的 ERS 诊断标志物。
基于基因表达综合数据库（GEO）中牙周炎相关微阵列数据集和先前研究中的 295 个 ERS，揭示差异表达的 ERSGs（DE-ERSGs），构建蛋白质-蛋白质相互作用网络。然后，探索牙周炎的亚型，并用免疫细胞浸润和基因集富集进行验证。使用两种机器学习算法揭示牙周炎潜在的 ERS 诊断标志物。进一步评估这些标志物的诊断效果、靶标药物和免疫相关性。最后，构建 miRNA-基因相互作用网络。
共揭示了 34 个 DE-ERSGs 存在于牙周炎样本和对照之间，然后对两个亚型进行了研究。两个亚型之间的 ERS 评分、免疫浸润和标志性富集有显著差异。然后，总共探索了 7 个 ERS 诊断标志物，包括 FCGR2B、XBP1、EDEM2、ATP2A3、ERLEC1、HYO1 和 YOD1，时间依赖的 ROC 分析显示了可靠的结果。此外，构建了一个包含 4 个上调的 ERS 诊断标志物和 24 种药物的药物-基因网络。最后，基于 32 个相互作用、5 个诊断标志物和 20 个 miRNAs，构建了一个 miRNA-靶标网络。
上调的 miR-671-5p 可能通过刺激 ATP2A3 的表达参与牙周炎的进展。XBP1 和 FCGR2B 等 ERSGs 可能是牙周炎的新型诊断标志物。
