Mou Lisha, Wang Tony Bowei, Lu Ying, Wu Zijing, Chen Yuxian, Luo Ziqi, Wang Xinyu, Pu Zuhui
Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China.
MetaLife Center, Shenzhen Institute of Translational Medicine, Shenzhen, Guangdong, China.
Front Immunol. 2025 Aug 14;16:1514243. doi: 10.3389/fimmu.2025.1514243. eCollection 2025.
Type 2 diabetes (T2D) is characterized by insulin resistance and chronic inflammation, with macrophages playing a crucial role in pancreatic islet dysfunction. This study explored the intersection of macrophage-specific gene expression and abnormal blood monovalent inorganic cation concentration-related genes (ABRGs) in T2D patients via single-cell RNA sequencing (scRNA-seq) and machine learning to identify key genes and potential therapeutic targets.
ScRNA-seq data from the pancreatic islet cells of 27 nondiabetic (ND) patients and 17 T2D patients were analyzed to identify differentially expressed genes (DEGs) in macrophages. These DEGs were intersected with ABRGs to identify hub genes. Machine learning models were developed to predict T2D, and structural predictions of the hub proteins were performed. PPI networks and regulatory networks involving transcription factors (TFs) and miRNAs were also analyzed. Correlations between hub ABRGs and immune cell infiltration, as well as cytokine responses, were examined via ssGSEA and immune response enrichment analysis (IREA).
Sixteen overlapping hub ABRGs, including , , and , were identified. The GBM model demonstrated high predictive accuracy, with an AUC of 0.988. Correlation analysis revealed significant relationships between the hub genes and the infiltration of immune cells, particularly macrophages. Cytokine enrichment analysis revealed that macrophages in T2D exhibit a distinct signature of cytokines, including IL15, IFNα1, IFNβ, and IL17F. PPI networks highlighted significant interactions among the hub genes. Regulatory network analysis revealed that STAT3 is a central TF and that miRNAs such as hsa-mir-1-3p are critical regulators.
This study highlights the central roles of macrophages and ABRGs in T2D, identifying novel genes and regulatory networks that contribute to disease progression. The integration of scRNA-seq and machine learning provides valuable insights and potential therapeutic targets for T2D.
2型糖尿病（T2D）的特征是胰岛素抵抗和慢性炎症，巨噬细胞在胰岛功能障碍中起关键作用。本研究通过单细胞RNA测序（scRNA-seq）和机器学习探索了T2D患者巨噬细胞特异性基因表达与异常血液单价无机阳离子浓度相关基因（ABRGs）的交集，以识别关键基因和潜在治疗靶点。
分析了27名非糖尿病（ND）患者和17名T2D患者胰岛细胞的scRNA-seq数据，以识别巨噬细胞中差异表达基因（DEGs）。这些DEGs与ABRGs进行交集分析以识别枢纽基因。开发机器学习模型来预测T2D，并对枢纽蛋白进行结构预测。还分析了涉及转录因子（TFs）和miRNAs的蛋白质-蛋白质相互作用（PPI）网络和调控网络。通过单样本基因集富集分析（ssGSEA）和免疫反应富集分析（IREA）检查枢纽ABRGs与免疫细胞浸润以及细胞因子反应之间的相关性。
鉴定出16个重叠的枢纽ABRGs，包括 、 和 。GBM模型显示出高预测准确性，曲线下面积（AUC）为0.988。相关性分析揭示了枢纽基因与免疫细胞浸润，特别是巨噬细胞浸润之间的显著关系。细胞因子富集分析表明，T2D中的巨噬细胞表现出独特的细胞因子特征，包括IL15、IFNα1、IFNβ和IL17F。PPI网络突出了枢纽基因之间的显著相互作用。调控网络分析表明，信号转导和转录激活因子3（STAT3）是核心TF，而诸如hsa-mir-1-3p等miRNAs是关键调节因子。
本研究突出了巨噬细胞和ABRGs在T2D中的核心作用，识别出有助于疾病进展的新基因和调控网络。scRNA-seq和机器学习的整合为T2D提供了有价值的见解和潜在治疗靶点。
