Multi-omics and machine learning identify FN1 and ALDH2 as diagnostic biomarkers and therapeutic targets in early and late diabetic kidney disease.

ABSTRACT: Diabetic kidney disease (DKD), the leading cause of end-stage kidney disease worldwide, demands deeper molecular characterization to improve clinical management. This study employed an integrated multi-omics approach to identify stage-specific biomarkers and molecular mechanisms distinguishing early- and late-stage DKD. Initial bulk RNA-seq analysis (thresholds: |logFC|>0.585, FDR < 0.05) revealed differentially expressed genes and enriched pathways, followed by two-sample Mendelian randomization (IVW p < 0.05) to pinpoint causal genes. Diagnostic modeling combined LASSO regression (10-fold cross-validation) with four machine learning algorithms (Random Forest, SVM, GLM, and XGBoost), validated in an independent cohort. Single-cell resolution analysis mapped candidate gene expressio

SUBMITTER: Lin J 

PROVIDER: S-EPMC12573553 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature