Tianjin Stomatological Hospital, School of Medicine, Nankai University, Tianjin 300000, China.
State Key Laboratory of Natural and Biomimetic Drugs, Peking University, Beijing 100191, China.
Oxid Med Cell Longev. 2022 Sep 13;2022:9728172. doi: 10.1155/2022/9728172. eCollection 2022.
METHODS: The differentially expressed genes (DEGs) were identified using periodontitis-related microarray from the GEO database, and OS-genes were extracted from GeneCards database. The intersection of the OS-genes and the DEGs was considered as oxidative stress-related DEGs (OS-DEGs) in periodontitis. The Pearson correlation and protein-protein interaction analyses were used to screen key OS-genes. Gene set enrichment, functional enrichment, and pathway enrichment analyses were performed in OS-genes. Based on key OS-genes, a risk score model was constructed through logistic regression, receiver operating characteristic curve, and stratified analyses. RESULTS: In total, 74 OS-DEGs were found in periodontitis, including 65 upregulated genes and 9 downregulated genes. Six of them were identified as key OS-genes (CXCR4, SELL, FCGR3B, FCGR2B, PECAM1, and ITGAL) in periodontitis. All the key OS-genes were significantly upregulated and associated with the increased risk of periodontitis. Functional enrichment analysis showed that these genes were mainly associated with leukocyte cell-cell adhesion, phagocytosis, and cellular extravasation. Pathway analysis revealed that these genes were involved in several signaling pathways, such as leukocyte transendothelial migration and osteoclast differentiation. CONCLUSION: In this study, we screened six key OS-genes that were screened as risk factors of periodontitis. We also identified multiple signaling pathways that might play crucial roles in regulating oxidative stress damage in periodontitis. In the future, more experiments need to be carried out to validate our current findings.
方法:从 GEO 数据库中牙周病相关的微阵列中鉴定差异表达基因(DEGs),并从 GeneCards 数据库中提取氧化应激基因(OS-genes)。将 OS-genes 和 DEGs 的交集视为牙周病中的氧化应激相关 DEGs(OS-DEGs)。采用 Pearson 相关性分析和蛋白质-蛋白质相互作用分析筛选关键 OS-genes。对 OS-genes 进行基因集富集、功能富集和通路富集分析。基于关键 OS-genes,通过逻辑回归、接收者操作特征曲线和分层分析构建风险评分模型。
结果:共发现牙周病中 74 个 OS-DEGs,包括 65 个上调基因和 9 个下调基因。其中 6 个被鉴定为牙周病中的关键 OS-genes(CXCR4、SELL、FCGR3B、FCGR2B、PECAM1 和 ITGAL)。所有关键 OS-genes 均显著上调,并与牙周病风险增加相关。功能富集分析表明,这些基因主要与白细胞细胞间黏附、吞噬作用和细胞外渗有关。通路分析表明,这些基因参与了白细胞跨内皮迁移和破骨细胞分化等多个信号通路。
结论:本研究筛选出了 6 个作为牙周病危险因素的关键 OS-genes,还鉴定了多个可能在调节牙周病氧化应激损伤中起关键作用的信号通路。未来需要进行更多实验来验证我们目前的发现。
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