Department of Nephrology, Second Hospital of Lanzhou University, Lanzhou 730030, Gansu, China.
Clinical Medical Research Center of Gansu Province(No. 21JR7RA436), Lanzhou 730030, Gansu, China.
Curr Diabetes Rev. 2024;21(2):e070524229720. doi: 10.2174/0115733998297749240418071555.
Diabetes mellitus (DM) frequently results in Diabetic Nephropathy (DN), which has a significant negative impact on the quality of life of diabetic patients. Sphingolipid metabolism is associated with diabetes, but its relationship with DN is unclear. Therefore, screening biomarkers related to sphingolipid metabolism is crucial for treating DN.
To identify Differentially Expressed Genes (DEGs) in the GSE142153 dataset, we conducted a differential expression analysis (DN samples versus control samples). The intersection genes were obtained by overlapping DEGs and Sphingolipid Metabolism-Related Genes (SMRGs). Furthermore, The Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithms were used to filter biomarkers. We further analyzed the Gene Set Enrichment analysis (GSEA) and the immunoinfiltrational analysis based on biomarkers.
We identified 2,186 DEGs associated with DN. Then, five SMR-DEGs were obtained. Subsequently, biomarkers associated with sphingolipid metabolism () were identified by applying machine learning and expression analysis. In addition, GSEA showed that these biomarkers were correlated with cytokine cytokine receptor interaction'. Significant variations in B cells, DCs, Tems, and Th2 cells between the two groups suggested that these cells might have a role in DN.
Overall, we obtained two sphingolipid metabolism-related biomarkers () associated with DN, which laid a theoretical foundation for treating DN.
糖尿病（DM）常导致糖尿病肾病（DN），对糖尿病患者的生活质量有重大负面影响。神经酰胺代谢与糖尿病有关，但与 DN 的关系尚不清楚。因此，筛选与神经酰胺代谢相关的生物标志物对于治疗 DN 至关重要。
为了在 GSE142153 数据集识别差异表达基因（DEGs），我们对 DN 样本与对照样本进行了差异表达分析。通过重叠 DEGs 和神经酰胺代谢相关基因（SMRGs）获得交集基因。此外，采用最小绝对收缩和选择算子（LASSO）和支持向量机递归特征消除（SVM-RFE）算法筛选生物标志物。我们进一步基于生物标志物分析基因集富集分析（GSEA）和免疫浸润分析。
我们鉴定了 2186 个与 DN 相关的 DEGs。然后，获得了 5 个 SMR-DEGs。随后，通过应用机器学习和表达分析，鉴定了与神经酰胺代谢相关的生物标志物（）。此外，GSEA 表明这些生物标志物与细胞因子细胞因子受体相互作用有关。两组间 B 细胞、DCs、Tems 和 Th2 细胞的显著差异表明这些细胞可能在 DN 中起作用。
总的来说，我们获得了与 DN 相关的两种神经酰胺代谢相关的生物标志物（），为治疗 DN 奠定了理论基础。
