Identification of biomarkers in diabetic neuropathy: a Mendelian randomization and bioinformatics analysis.

Abstract

BACKGROUND: This study elucidated several plasma proteins that are causally linked to the risk of diabetic neuropathy (DN), offering novel insights into the protein-mediated DN pathogenesis and potential targets for therapeutic intervention. METHODS: We employed Mendelian randomization (MR) utilizing genome-wide association study (GWAS) data to evaluate the causal effects of 4,907 proteins on DN. We retrieved a high-throughput sequencing dataset (GSE148061, containing 53 DN patients and 53 healthy donors) from the Gene Expression Omnibus (GEO) database to perform differential gene analysis and functional enrichment analysis, aiming to clarify disease pathogenesis. The MR findings were subsequently validated through Bayesian colocalization analysis and cluster identification. We utilized two machine learning algorithms: Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF). Further, Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were conducted on these key genes to uncover the underlying molecular mechanisms influencing DN. Finally, we constructed a DN mouse model to validate the diagnostic potential of these identified genes. RESULTS: We identified seven plasma proteins significantly associated with DN. By integrating Bayesian colocalization, various datasets, and two machine learning methods, we determined that the HSPB1, KRT14, and SFN genes are the most robust diagnostic biomarkers and key mediators. These genes were primarily enriched in the Wnt Signaling Pathway, Regulation of ERK1 and Cascade, Negative Regulation of MAPK Cascade, and Alditol Phosphate Metabolic Process. The expression changes of HSPB1, KRT14, and SFN were further validated using animal models. CONCLUSION: This study systematically identified HSPB1, KRT14, and SFN as potential biomarkers for patients with DN.