Genomic mapping of diabetic kidney disease biomarkers and identification of potential inhibitors through virtual screening.

BACKGROUND

Diabetic kidney disease (DKD) is a common and serious complication of diabetes mellitus, marked by a multifactorial pathogenesis and the absence of sensitive diagnostic biomarkers. Identifying novel molecular targets and therapeutic options is essential to improve early diagnosis and treatment outcomes.

METHODS

To uncover potential biomarkers and therapeutic candidates, we performed an integrated genomic analysis using microarray and RNA-seq datasets from the Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA) databases. Differentially expressed genes (DEGs) were identified and subjected to protein-protein interaction (PPI) network analysis. Key genes were further explored through virtual screening of an FDA-approved compound library using molecular docking techniques. Drug-likeness was assessed via Lipinski's rule of five.

RESULTS

A total of 40 DEGs were identified, among which ISCU (downregulated; involved in iron-sulfur cluster biogenesis) and AP1S2 (upregulated; associated with vesicular trafficking) emerged as potential biomarkers. PPI analysis revealed their involvement in critical DKD-related pathways, such as extracellular matrix remodeling and oxidative stress. Virtual screening identified six FDA-approved compounds with high binding affinity (≤-7.96 kcal/mol) to ISCU, notably ZINC000001576020, all of which complied with Lipinski's rule.

CONCLUSIONS

This in-silico study nominates ISCU and AP1S2 as candidate diagnostic biomarkers for DKD and identifies computationally prioritized inhibitors targeting ISCU. These findings require experimental validation but provide a molecular framework for precision diagnosis and therapeutic development. These findings offer new molecular insights that could inform precision diagnosis and personalized treatment strategies for diabetic kidney disease.