Identification of hub genes involved in the pathogenesis of diabetic nephropathy: A multi‐omics study integrating machine learning, mendelian randomization and mediation analysis

BACKGROUND: Diabetic nephropathy (DN), affecting 30%-40% of diabetic patients, is the leading cause of end-stage renal disease worldwide. This study aims to identify diagnostic biomarkers and explore potential gene-metabolite interactions in DN pathogenesis through integrated bioinformatics approaches and experimental validation. METHODS: We analysed Gene Expression Omnibus datasets through differential expression analysis, weighted gene co-expression network analysis (WGCNA) and machine learning algorithms to identify key DN-associated genes. The causal relationships between candidate genes and DN were investigated using Mendelian randomization (MR) analysis with cis-expression quantitative trait loci data, which random-effects inverse variance weighted (IVW) method was our primary analysis approach. We then employed mediating MR analysis to explore metabolite-mediated pathways. The expression of identified genes was validated in both human DN kidney samples and diabetic mouse models. RESULTS: Our analysis identified seven diagnostic genes (LUM, ESM1, VCAN, LOX, G0S2, THBS2 and UMOD). Through MR analysis, THBS2 showed a significant causal association with DN outcomes (OR = 1.1653, 95% CI [1.0103, 1.3441], p = 0.0357). Subsequent mediation analysis suggested that THBS2 may influence DN development through modulation of N-acetyl-beta-alanine levels (mediating effect = 17.85%, p = 0.02). Importantly, elevated THBS2 expression was confirmed in both DN patient kidney samples and diabetic mouse models, validating our bioinformatics findings. CONCLUSIONS: Through comprehensive bioinformatics analysis and experimental validation, we identified THBS2 as a potential diagnostic marker for DN. These findings provide new insights into DN pathogenesis and suggest directions for future therapeutic strategies.