Bioinformatics to Identify Biomarkers of Diabetic Nephropathy based on Sphingolipid Metabolism and their Molecular Mechanisms

Background: 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. Methods: 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. Results: We identified 2,186 DEGs associated with DN. Then, five SMR-DEGs were obtained. Subsequently, biomarkers associated with sphingolipid metabolism (S1PR1 and SELL) 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. Conclusion:: Overall, we obtained two sphingolipid metabolism-related biomarkers (S1PR1 and SELL) associated with DN, which laid a theoretical foundation for treating DN.