Integrative transcriptomic and genomic insights into diabetic kidney disease: evidence from multi-omics analysis and experimental validation.

ABSTRACT: Diabetic kidney disease (DKD) remains a critical challenge in diabetes management, necessitating a deep understanding of its molecular underpinnings for better diagnosis and treatment strategies. This study was conducted to identify and validate novel biomarkers for DKD by integrating multi-omics analysis and experimental validation. Through weighted gene co-expression network analysis and Mendelian randomization analysis, 11 genes were identified as being causally associated with DKD. ADARB2, GOLPH3L, LRG1, and PEX6 were identified as characteristic genes through machine learning methods, including least absolute shrinkage and selection operator (LASSO) regression and SVM algorithms. Receiver operating characteristic curve analysis demonstrated that the characteristic genes had high predi

SUBMITTER: Chen S 

PROVIDER: S-EPMC12532357 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature