Table 3_Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence.xlsx

BackgroundDiabetic nephropathy (DN) is a complication of systemic microvascular disease in diabetes mellitus. Abnormal glycolysis has emerged as a potential factor for chronic renal dysfunction in DN. The current lack of reliable predictive biomarkers hinders early diagnosis and personalized therapy. MethodsTranscriptomic profiles of DN samples and controls were extracted from GEO databases. Differentially expressed genes (DEGs) and their functional enrichments were identified. Glycolysis-related genes (GRGs) were selected by combining DEGs, weighted gene co-expression network, and glycolysis candidate genes. We established a diagnostic signature termed GScore via integrative machine learning framework. The diagnostic efficacy was evaluated by decision curve and calibration curve. Single-cell RNA sequence data was used to identify cell subtypes and interactive signals. The cMAP database was used to find potential therapeutic agents targeting GScore for DN. The expression levels of diagnostic signatures were verified in vitro. ResultsThrough the 108 combinations of machine learning algorithms, we selected 12 diagnostic signatures, including CD163, CYBB, ELF3, FCN1, PROM1, GPR65, LCN2, LTF, S100A4, SOX4, TGFB1 and TNFAIP8. Based on them, an integrative model named GScore was established for predicting DN onset and stratifying clinical risk. We observed distinct biological characteristics and immunological microenvironment states between the high-risk and low-risk groups. GScore was significantly associated with neutrophils and non-classical monocytes. Potential agents including esmolol, estradiol, ganciclovir, and felbamate, targeting the 12 diagnostic signatures were identified. In vitro, ELF3, LCN2 and CD163 were induced in high glucose-induced HK-2 cell lines. ConclusionAn integrative machine learning frame established a novel diagnostic signature using glycolysis-related genes. This study provides a new direction for the early diagnosis and treatment of DN.背景：糖尿病肾病（Diabetic nephropathy, DN）是糖尿病患者并发的全身微血管病变。糖酵解异常已被证实为DN患者发生慢性肾功能不全的潜在致病因素。目前缺乏可靠的预测生物标志物，这一局限性阻碍了DN的早期诊断与个体化治疗。 方法：从GEO数据库中提取DN样本与对照样本的转录组表达谱。鉴定差异表达基因（DEGs）及其功能富集结果。通过整合差异表达基因、加权基因共表达网络与糖酵解候选基因，筛选得到糖酵解相关基因（GRGs）。借助整合机器学习框架构建名为GScore的诊断特征模型。采用决策曲线与校准曲线评估该模型的诊断效能。利用单细胞RNA测序数据识别细胞亚型与细胞间互作信号。通过cMAP数据库筛选针对GScore的DN潜在治疗药物，并在体外实验中验证诊断特征的表达水平。 结果：通过对108种机器学习算法组合的筛选，最终选定12个诊断特征基因，分别为CD163、CYBB、ELF3、FCN1、PROM1、GPR65、LCN2、LTF、S100A4、SOX4、TGFB1与TNFAIP8。基于上述基因构建名为GScore的整合模型，用于预测DN发病风险并进行临床风险分层。分析显示，高风险组与低风险组呈现显著不同的生物学特征与免疫微环境状态。GScore与中性粒细胞及非经典单核细胞水平显著相关。筛选得到靶向该12个诊断特征基因的潜在治疗药物，包括艾司洛尔、雌二醇、更昔洛韦与非尔氨酯。体外实验证实，高糖诱导的HK-2细胞系中ELF3、LCN2与CD163的表达水平显著上调。 结论：本研究通过整合机器学习框架，依托糖酵解相关基因构建了全新的DN诊断特征模型，为糖尿病肾病的早期诊断与治疗提供了新的研究方向。