Journal Article

Tracing the molecular landscape of diabetic nephropathy: Insights from machine learning and experiment verification

Alhasani, Reem Hasaballah, Basri, Ahmed M., Elhawary, Nasser A., Mufti, Ahmad H., Abumansour, Iman S., Alhashmi, Mohammad H., Almars, Amany I., Sindi, Ikhlas A., Almohaimeed, Hailah M., Kattan, Shahad W., Alsharif, Ifat, Qahtani, Aishah Abdullah

2025

Overview

Objective Diabetes is a chronic disease resulting from insufficient insulin secretion or impaired insulin function. Diabetic nephropathy (DN) is one of the most common complications of diabetes and a leading cause of end‐stage renal disease. Early diagnosis of DN is crucial for timely intervention and effective disease management. Methods Gene expression profiles GSE142025 and GSE220226 were retrieved from the GEO database and combined into a metadata cohort, while GSE189007 was obtained as an independent validation dataset. Differentially expressed genes (DEGs) were identified in 46 glomerular samples from DN patients and 31 control samples. Gene Ontology (GO) and Disease Ontology (DO) enrichment analyses, gene set enrichment analysis (GSEA), least absolute shrinkage and selection operator (LASSO) regression, support vector machine‐recursive feature elimination (SVM‐RFE) analysis, and area under the curve (AUC) calculations were performed. Results A total of 109 DEGs were identified. Among them, DUSP1, EGR1, FPR1, G6PC, GDF15, LOX, LPL, PRKAR2B, PTGDS, and TPPP3 were selected as potential diagnostic biomarkers for DN. These biomarkers exhibited a positive correlation with immune cell infiltration. Experimental validation identified LOX as the most promising novel diagnostic biomarker for DN. This study provides new insights into the early diagnosis, pathogenesis, and molecular mechanisms of DN. Diabetic nephropathy (DN) is a major complication of diabetes, leading to end‐stage renal disease. This study identified 109 differentially expressed genes (DEGs) and validated LOX as a novel diagnostic biomarker through integrated bioinformatics and experimental analysis. The findings highlight key molecular mechanisms and immune cell interactions, offering new insights for early DN diagnosis and treatment.

• Publisher: John Wiley & Sons, Inc,John Wiley and Sons Inc,Wiley
• Subject: Biomarkers / Biomarkers - analysis / Case-Control Studies / Correlation analysis / Datasets / Diabetes / Diabetes mellitus / Diabetic Nephropathies - diagnosis / Diabetic Nephropathies - genetics / Diabetic Nephropathies - metabolism / Diabetic nephropathy / Diagnosis / Diagnostic Biomarkers / EGR-1 protein / Epigenetics / Gene expression / Gene Expression Profiling / Gene Ontology / Gene set enrichment analysis / Hemodynamics / Humans / Hyperglycemia / Immune Cell Infiltration / Insulin secretion / Kidney diseases / Machine Learning / Metabolism / Molecular modelling / Nephropathy / Original / Pathogenesis / Proteins / Software / Transcriptome