College of Stomatology, Chongqing Medical University, China; Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, China; Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, China.
Department of Laboratory Medicine, Key Laboratory of Diagnostic Medicine (Ministry of Education), Chongqing Medical University, Chongqing, China.
Cytokine. 2022 Nov;159:156014. doi: 10.1016/j.cyto.2022.156014. Epub 2022 Sep 7.
This bioinformatics study is aimed at identifying cross-talk genes, pyroptosis-related genes, and related pathways between periodontitis (PD) and diabetes mellitus (DM), which includes type 1 diabetes (T1DM) and type 2 diabetes (T2DM).
GEO datasets containing peripheral blood mononuclear cell (PBMC) data of PD and DM were acquired. After batch correction and normalization, differential expression analysis was performed to identify the differentially expressed genes (DEGs). And cross-talk genes in the PD-T1DM pair and the PD-T2DM pair were identified by overlapping DEGs with the same trend in each pair. The weighted gene coexpression network analysis (WGCNA) algorithm helped locate the pyroptosis-related genes that are related to cross-talk genes. Receiver-operating characteristic (ROC) curve analysis confirmed the predictive accuracy of these hub genes in diagnosing PD and DM. The correlation between hub genes and the immune microenvironment of PBMC in these diseases was investigated by Spearman correlation analysis. The experimentally validated protein-protein interaction (PPI) and gene-pathway network were constructed. Subnetwork analysis helped identify the key pathway connecting DM and PD.
Hub genes in the PD-T1DM pair (HBD, NLRC4, AIM2, NLRP2) and in the PD-T2DM pair (HBD, IL-1Β, AIM2, NLRP2) were identified. The similarity and difference in the immunocytes infiltration levels and immune pathway scores of PD and DM were observed. ROC analysis showed that AIM2 and HBD exhibited pleasant discrimination ability in all diseases, and the subnetwork of these genes indicated that the NOD-like receptor signaling pathway is the most potentially relevant pathway linking PD and DM.
HBD and AIM2 could be the most relevant potential cross-talk and pyroptosis-related genes, and the NOD-like receptor signaling pathway could be the top candidate molecular mechanism linking PD and DM, supporting a potential pathophysiological relationship between PD and DM.
本生物信息学研究旨在鉴定牙周炎（PD）和糖尿病（DM）之间的串扰基因、细胞焦亡相关基因和相关通路，其中包括 1 型糖尿病（T1DM）和 2 型糖尿病（T2DM）。
获取包含 PD 和 DM 外周血单个核细胞（PBMC）数据的 GEO 数据集。经过批次校正和归一化后，进行差异表达分析以鉴定差异表达基因（DEGs）。通过重叠每个对中具有相同趋势的 DEGs，鉴定 PD-T1DM 对和 PD-T2DM 对中的串扰基因。加权基因共表达网络分析（WGCNA）算法有助于定位与串扰基因相关的细胞焦亡相关基因。接受者操作特征（ROC）曲线分析证实了这些枢纽基因在诊断 PD 和 DM 中的预测准确性。通过 Spearman 相关性分析研究了这些疾病中枢纽基因与 PBMC 免疫微环境的相关性。构建了经过实验验证的蛋白质-蛋白质相互作用（PPI）和基因通路网络。子网络分析有助于确定连接 DM 和 PD 的关键通路。
鉴定了 PD-T1DM 对（HBD、NLRC4、AIM2、NLRP2）和 PD-T2DM 对（HBD、IL-1β、AIM2、NLRP2）中的枢纽基因。观察到 PD 和 DM 中免疫细胞浸润水平和免疫途径评分的相似性和差异。ROC 分析表明，AIM2 和 HBD 在所有疾病中均表现出良好的区分能力，这些基因的子网络表明 NOD 样受体信号通路是连接 PD 和 DM 最相关的潜在通路。
HBD 和 AIM2 可能是最相关的潜在串扰和细胞焦亡相关基因，NOD 样受体信号通路可能是连接 PD 和 DM 的最重要候选分子机制，支持 PD 和 DM 之间存在潜在的病理生理关系。
