Patients with diabetes had a significantly increased risk of cardiovascular disease by reporter, and the nebulette (NEBL) gene were closely related with cardiovascular disease. However, the impact of the NEBL gene on diabetic nephropathy (DN) and the underlying molecular mechanisms, have yet to be conclusively validated. Therefore, this study aims to mine NEBL related biomarkers in DN by bioinformatics analysis. A total of 157 differentially expressed genes (DEGs) associated with DN and NEBL gene were excavated, and they were associated with biological processes of mesonephric development and AGE-RAGE signaling pathway in diabetic complications. Besides, totally 19 candidate genes were screened by correlation and expression analyses, among which, MPP5, TGFBR3, PCMTD2 and C1orf21 related to NEBL were deemed as biomarkers via Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Boruta and least absolute shrinkage and selection operator (LASSO) algorithms, the ability of NEBL and biomarkers to distinguish DN from controls was accurate. Immunoinfiltration certified that the contents of 12 differential immune cells were significantly increased in DN group. Then, the gene set enrichment analysis (GSEA) revealed that NEBL and biomarkers were observably enriched in ribosome, intestinal immune network for immunoglobulin A (IgA) production and so on. Finally, drug-gene network revealed bisphenol A and Valproic Acid might be pivotal drugs regulating the expression of NEBL and biomarkers.In this sutudy, totally four biomarkers (MPP5, TGFBR3, PCMTD2 and C1orf21) related to NEBL were screened by bioinformatic analysis, providing a novel reference for effective clinical diagnosis and treatment of DN.