Du Qianqian, Liu Kun, Li Yanling, Wang Xinyan, Liu Xin, Zhao Jing, Wang Xuemei
School of Public Health, Inner Mongolia Medical University, Huhhot, China.
Front Endocrinol (Lausanne). 2025 Aug 21;16:1617292. doi: 10.3389/fendo.2025.1617292. eCollection 2025.
Type 2 diabetes (T2DM) and tuberculosis (TB) both regulate inflammation and may exert synergistic or antagonistic effects through shared immune pathways. Previous studies have demonstrated that T2DM is a risk factor for TB. However, at the level of gene regulatory networks, it remains unclear whether there are key interaction nodes linking these two diseases. In this study, we integrated bioinformatic analysis from the Gene Expression Omnibus (GEO) database and performed differential gene expression analysis and weighted gene co-expression network analysis (WGCNA). Furthermore, we applied machine learning techniques to identify key genes among the commonly differentially expressed genes (DEGs). In addition, this study employed siRNA in THP-1 cells to validate the cross-talk genes selected through bioinformatic analysis. The THP-1 cells were treated with high-concentration glucose (15.5 μM, Glu), ESAT-6, or Glu+ESAT-6. We identified a total of 23 common genes between TB and T2DM using DEGs and WGCNA. Furthermore, expression patterns from external datasets revealed three key cross-talk genes linking TB-T2DM: , , and . Notably, only was significantly upregulated in the THP-1 detection test, compared to the unstimulated (control) group ( < 0.05). Moreover, significantly reduced the expression of pro-inflammatory cytokines (TNF-α, IL-6, IL-1β, IL-10), M2 macrophage polarization markers (CD163, Arg-1), and chemokines (CXCL-10), and was associated with the NOD2 and TRAF6 signaling pathways ( < 0.05). These findings elucidate the regulatory mechanisms underlying the comorbidity of TB and T2DM, providing a theoretical basis for the development of precise combination therapies and novel therapeutic targets.
2型糖尿病(T2DM)和结核病(TB)均调控炎症反应,并可能通过共享的免疫途径发挥协同或拮抗作用。既往研究表明,T2DM是TB的一个危险因素。然而,在基因调控网络层面,尚不清楚是否存在连接这两种疾病的关键相互作用节点。在本研究中,我们整合了来自基因表达综合数据库(GEO)的生物信息学分析,并进行了差异基因表达分析和加权基因共表达网络分析(WGCNA)。此外,我们应用机器学习技术在共同差异表达基因(DEG)中识别关键基因。此外,本研究在THP-1细胞中使用小干扰RNA(siRNA)来验证通过生物信息学分析选择的相互作用基因。THP-1细胞用高浓度葡萄糖(15.5 μM,Glu)、早期分泌性抗原靶6(ESAT-6)或Glu + ESAT-6处理。我们使用DEG和WGCNA在TB和T2DM之间共鉴定出23个共同基因。此外,来自外部数据集的表达模式揭示了连接TB-T2DM的三个关键相互作用基因: 、 和 。值得注意的是,在THP-1检测试验中,与未刺激(对照)组相比,只有 在刺激后显著上调( < 0.05)。此外, 显著降低促炎细胞因子(TNF-α、IL-6、IL-1β、IL-10)、M2巨噬细胞极化标志物(CD163、精氨酸酶1)和趋化因子(CXCL-10)的表达,并且与核苷酸结合寡聚化结构域样受体蛋白2(NOD2)和肿瘤坏死因子受体相关因子6(TRAF6)信号通路相关( < 0.05)。这些发现阐明了TB和T2DM合并症的调控机制,为精准联合治疗的开发和新的治疗靶点提供了理论依据。