Immune Infiltration and Mitochondrial Function in Diabetic Kidney Disease: WGCNA and Machine Learning Identified Hub...

Diabetic kidney disease (DKD) lacks specific biomarkers reflecting the interplay between mitochondrial dysfunction and immune microenvironment remodeling. To address this, we integrated multi-dataset transcriptomics (GEO, MitoCarta 3.0, GeneCards) with Weighted Gene Co-expression Network Analysis, protein-protein interaction networks, and machine learning algorithms to identify key diagnostic genes. Single-nucleus RNA sequencing was utilized to map cell-type distributions. Subsequently, a single-center cohort of 70 biopsy-confirmed DKD patients was enrolled for validation of the key hub gene, HDAC6. We identified four hub genes: EGF (downregulated), HDAC6, TPM1, and VCAM1 (upregulated). All genes exhibited robust diagnostic efficacy, and single-nucleus analysis revealed distinct renal cell-type enrichment patterns. Clinically, high renal HDAC6 expression correlated with severe interstitial inflammation, elevated complement C3 and cystatin C, and reduced urinary ammonium (a clinical proxy for proximal tubular mitochondrial dysfunction). Crucially, high HDAC6 served as an independent risk factor for both renal endpoints and cardiorenal composite events. In conclusion, EGF, HDAC6, TPM1, and VCAM1 are key regulators in DKD. Specifically, intrarenal HDAC6 quantification serves as a precise histological metric for prognostic stratification and underscores its potential as a therapeutic target for DKD intervention.