Affiliation:
1. Department of Nephrology, Second Hospital of Lanzhou University, Lanzhou 730030, Gansu, China
Abstract
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.
Publisher
Bentham Science Publishers Ltd.