BACKGROUND: Diabetes Mellitus type 2 (DM2) is thought to have a bidirectional relationship with Periodontitis (PD). However, the complex molecular interactions between DM2 and PD remain unclear. This study aimed to explore the shared genes and common signatures of DM2 and PD via bioinformatic analysis. METHODS: Firstly, using bioinformatic methods to investigate common genes. The series matrix files of GSE6751 for DM and GSE15932 for PD were downloaded from the Gene Expression Omnibus (GEO) database. The data was normalized using the R package, and the limma package was utilized to identify the Differentially Expressed Genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of DEGs were performed using the "clusterProfiler" package in the R software. The protein-protein network was constructed to analyze the potential relationship among the proteins. CytoHubba, a plugin for the Cytoscape software, was used to identify the hub genes. The validation datasets selected for DM2 and PD were GSE10334 and GSE7014, respectively. Receiver Operating Characteristic (ROC) curve analysis was performed to obtain the area under the ROC curve. Lipopolysaccharide (LPS) +â high glucose-induced DM-related PD was simulated to verify the three hub genes through quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) and Western blot (WB). RESULTS: In total, 44 common DEGs were identified. ITGAM, H2BC21, S100A9 was identified as he hub genes of DM2 and PD, with all of them were up-regulated. In addition, the area under the curve of all three hub genes was more than 0.65. In-vitro experiments revealed that the relative expression of S100A9 was increased after the treatment with LPS +â high glucose. Besides, TLR4 and p-NF-κB levels were also improved in model group. CONCLUSION: S100A9 was identified as the hub gene of DM2 and PD. S100A9 could trigger TLR4 signaling way to promote disease development, which can be the potential targets for diagnosis and treatment.
Potential diagnostic markers and therapeutic targets for DM2 and periodontitis based on bioinformatics analysis.
基于生物信息学分析的DM2和牙周炎的潜在诊断标志物和治疗靶点
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作者:Luo Rong, Liang Zhenye, Chen Huijun, Bao Dandan, Lin Xinlu
| 期刊: | PLoS One | 影响因子: | 2.600 |
| 时间: | 2025 | 起止号: | 2025 Apr 2; 20(4):e0320061 |
| doi: | 10.1371/journal.pone.0320061 | 研究方向: | 炎症/感染 |
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