Raw qPCR Ct values.

Background Diabetic kidney disease (DKD) is a severe global complication of diabetes, yet its molecular mechanisms remain incompletely understood. This study aimed to investigate the role of protein glycosylation in DKD pathogenesis and its association with gene expression changes, with the goal of identifying diagnostic biomarkers and personalized therapeutic targets. Methods Integrated bioinformatics and machine learning approaches were applied to analyze multiple gene expression datasets. Differentially expressed glycosylation-related genes were identified, followed by unsupervised clustering to define molecular subtypes. Functional enrichment, immune cell infiltration analysis, and machine learning algorithms (including feature selection for hub genes) were employed. qPCR validation was performed on clinical DKD and normal kidney tissues, and ROC curves were generated to assess diagnostic potential. Results Unsupervised clustering of glycosylation-related genes revealed two distinct DKD molecular subtypes with differential pathway activation (e.g., extracellular matrix remodeling) and immune infiltration patterns. Six hub genes (S100A12, EXT1, SBSPON, ADAMTS1, FMOD, SPTB) were identified as critical to DKD pathogenesis through machine learning. Immune infiltration analysis showed significant differences in macrophage and neutrophil activity between DKD and controls and Immunohistochemical results confirmed the occurrence of immune infiltration. qPCR validation confirmed dysregulation of hub genes in DKD tissues compared to normal samples. ROC analysis demonstrated high diagnostic accuracy for these genes. Conclusions This study highlights abnormal protein glycosylation as a key player in DKD and identifies six hub genes with potential as diagnostic biomarkers. The molecular subtypes and immune infiltration patterns provide insights into disease heterogeneity, paving the way for personalized therapies. Future studies should validate these findings in larger cohorts with explicit sample sizes to strengthen clinical applicability.背景 糖尿病肾病（Diabetic Kidney Disease, DKD）是一种危害严重的全球性糖尿病并发症，但其分子机制仍未完全阐明。本研究旨在探讨蛋白质糖基化（protein glycosylation）在DKD发病机制中的作用及其与基因表达变化的关联，以期筛选诊断性生物标志物与个性化治疗靶点。 方法 本研究采用整合生物信息学与机器学习方法，对多组基因表达数据集进行分析。首先鉴定差异表达的糖基化相关基因，随后通过无监督聚类明确DKD的分子亚型。研究运用了功能富集分析、免疫细胞浸润分析以及包含核心基因特征筛选的机器学习算法。针对临床DKD组织与正常肾组织开展实时定量聚合酶链反应（qPCR）验证，并绘制受试者工作特征曲线（Receiver Operating Characteristic curve, ROC）以评估其诊断潜力。 结果 对糖基化相关基因的无监督聚类结果显示，存在两种具有显著差异通路激活（如细胞外基质重塑）与免疫浸润模式的DKD分子亚型。通过机器学习筛选出6个核心基因（S100A12、EXT1、SBSPON、ADAMTS1、FMOD、SPTB），它们在DKD发病机制中发挥关键作用。免疫细胞浸润分析表明，DKD组与对照组的巨噬细胞、中性粒细胞活性存在显著差异，免疫组化结果证实了免疫浸润的存在。qPCR验证结果证实，相较于正常样本，DKD组织中核心基因的表达存在失调。ROC曲线分析显示，这些基因具有较高的诊断准确性。 结论 本研究证实异常蛋白质糖基化是DKD发生发展的关键调控因素，并筛选出6个具备诊断生物标志物潜力的核心基因。本次鉴定的分子亚型与免疫浸润模式为理解疾病异质性提供了新视角，为个性化治疗奠定了基础。未来研究应在更大样本量的明确队列中验证本研究结果，以增强其临床应用价值。