Table 1_Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning.xlsx

IntroductionType 2 diabetes (T2D) is a complex metabolic disorder with significant global health implications. Understanding the molecular mechanisms underlying T2D is crucial for developing effective therapeutic strategies. This study employs single-cell RNA sequencing (scRNA-seq) and machine learning to explore the the pathogenesis of T2D, with a particular focus on immune cell infiltration.
MethodsWe analyzed scRNA-seq data from islet cells of T2D and nondiabetic (ND) patients, identifying differentially expressed genes (DEGs), especially those related to metal ion transport (RMITRGs). We employed 12 machine learning algorithms to develop predictive models and assessed immune cell infiltration using single-sample gene set enrichment analysis (ssGSEA). Correlations between immune cells and key RMITRGs were investigated, and the interactions among these genes were explored through protein-protein interaction (PPI) network analysis. Additionally, we performed a detailed cell-cell communication analysis to identify significant signaling pathways in T2D.
ResultsOur analysis identified 1953 DEGs between T2D and ND patients, with the Stepglm[backward] plus GBM model demonstrating high predictive accuracy and identifying 13 hub RMITRGs. Twelve protein structures were predicted using AlphaFold 3, revealing potential functional conformations. We observed a strong correlation between hub RMITRGs and immune cells, and PPI network analysis revealed key interactions. Cell-cell communication analysis highlighted 16 active signaling pathways, with CXCL, MIF, and COMPLEMENT linked to immune and inflammatory responses, and WNT, KIT, LIFR, and HGF pathways uniquely activated in T2D.
ConclusionOur analysis identified genes crucial for T2D, emphasizing ion transport, signaling, and immune cell interactions. These findings suggest therapeutic potential to enhance T2D management. The identified pathways and genes provide valuable insights into the disease mechanisms and potential targets for intervention.

引言
2型糖尿病（Type 2 diabetes, T2D）是一种复杂的代谢紊乱性疾病，对全球公共卫生具有深远影响。阐明T2D的分子发病机制，对于开发高效的治疗策略至关重要。本研究采用单细胞RNA测序（single-cell RNA sequencing, scRNA-seq）与机器学习方法，探究T2D的发病机制，重点关注免疫细胞浸润情况。

方法
我们分析了T2D患者与非糖尿病（nondiabetic, ND）患者胰岛细胞的scRNA-seq数据，筛选得到差异表达基因（differentially expressed genes, DEGs），尤其是与金属离子转运相关的差异基因（RMITRGs）。本研究采用12种机器学习算法构建预测模型，并通过单样本基因集富集分析（single-sample gene set enrichment analysis, ssGSEA）评估免疫细胞浸润水平。此外，我们探究了免疫细胞与关键RMITRGs之间的相关性，并通过蛋白质-蛋白质相互作用（protein-protein interaction, PPI）网络分析，揭示上述基因间的相互作用机制。同时，我们开展了细致的细胞间通讯分析，以识别T2D中具有重要生物学意义的信号通路。

结果
本研究在T2D与ND患者的胰岛细胞中筛选得到1953个DEGs，其中Stepglm[backward]联合GBM模型展现出优异的预测准确性，并筛选出13个核心RMITRGs。我们通过AlphaFold 3预测了12种蛋白质的空间结构，揭示了其潜在的功能构象。我们观察到核心RMITRGs与免疫细胞之间存在显著的相关性，PPI网络分析也明确了关键的基因相互作用关系。细胞间通讯分析共识别出16条活跃的信号通路，其中CXCL、MIF及COMPLEMENT通路与免疫及炎症反应密切相关，而WNT、KIT、LIFR及HGF通路则为T2D所特有激活。

结论
本研究明确了与T2D相关的关键基因，重点强调了离子转运、信号传导及免疫细胞相互作用在疾病进程中的核心作用。本研究结果为改善T2D的临床管理提供了潜在的治疗方向，所识别的通路与基因可为阐明T2D的发病机制及开发干预靶点提供宝贵的研究依据。