Stomatological Hospital, Southern Medical University, Guangzhou, China.
School of Stomatology, Southern Medical University, Guangzhou, China.
BMC Oral Health. 2020 Oct 12;20(1):279. doi: 10.1186/s12903-020-01266-5.
Pulpitis is an inflammatory disease, the grade of which is classified according to the level of inflammation. Traditional methods of evaluating the status of dental pulp tissue in clinical practice have limitations. The rapid and accurate diagnosis of pulpitis is essential for determining the appropriate treatment. By integrating different datasets from the Gene Expression Omnibus (GEO) database, we analysed a merged expression matrix of pulpitis, aiming to identify biological pathways and diagnostic biomarkers of pulpitis.
By integrating two datasets (GSE77459 and GSE92681) in the GEO database using the sva and limma packages of R, differentially expressed genes (DEGs) of pulpitis were identified. Then, the DEGs were analysed to identify biological pathways of dental pulp inflammation with Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Set Enrichment Analysis (GSEA). Protein-protein interaction (PPI) networks and modules were constructed to identify hub genes with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape.
A total of 470 DEGs comprising 394 upregulated and 76 downregulated genes were found in pulpitis tissue. GO analysis revealed that the DEGs were enriched in biological processes related to inflammation, and the enriched pathways in the KEGG pathway analysis were cytokine-cytokine receptor interaction, chemokine signalling pathway and NF-κB signalling pathway. The GSEA results provided further functional annotations, including complement system, IL6/JAK/STAT3 signalling pathway and inflammatory response pathways. According to the degrees of nodes in the PPI network, 10 hub genes were identified, and 8 diagnostic biomarker candidates were screened: PTPRC, CD86, CCL2, IL6, TLR8, MMP9, CXCL8 and ICAM1.
With bioinformatics analysis of merged datasets, biomarker candidates of pulpitis were screened and the findings may be as reference to develop a new method of pulpitis diagnosis.
牙髓炎症是一种炎症性疾病，其严重程度根据炎症程度进行分类。传统的临床评估牙髓组织状态的方法存在局限性。快速准确地诊断牙髓炎对于确定适当的治疗至关重要。通过整合来自基因表达综合数据库（GEO）的不同数据集，我们分析了牙髓炎的合并表达矩阵，旨在确定牙髓炎的生物学途径和诊断生物标志物。
使用 R 中的 sva 和 limma 包整合 GEO 数据库中的两个数据集（GSE77459 和 GSE92681），鉴定牙髓炎的差异表达基因（DEGs）。然后，通过基因本体论（GO）分析、京都基因与基因组百科全书（KEGG）通路富集分析和基因集富集分析（GSEA）对 DEGs 进行分析，以确定牙髓炎症的生物学途径。使用 Search Tool for the Retrieval of Interacting Genes/Proteins（STRING）和 Cytoscape 构建蛋白质-蛋白质相互作用（PPI）网络和模块，以识别关键基因。
在牙髓组织中发现了总共 470 个差异表达基因，其中 394 个上调，76 个下调。GO 分析表明，DEGs 富集于与炎症相关的生物过程中，KEGG 通路分析中富集的通路为细胞因子-细胞因子受体相互作用、趋化因子信号通路和 NF-κB 信号通路。GSEA 结果提供了进一步的功能注释，包括补体系统、IL6/JAK/STAT3 信号通路和炎症反应途径。根据 PPI 网络中节点的程度，确定了 10 个关键基因，并筛选出 8 个诊断生物标志物候选物：PTPRC、CD86、CCL2、IL6、TLR8、MMP9、CXCL8 和 ICAM1。
通过对合并数据集进行生物信息学分析，筛选出牙髓炎的生物标志物候选物，这些发现可能为开发新的牙髓炎诊断方法提供参考。
