APOC1 as a novel diagnostic biomarker for DN based on machine learning algorithms and experiment

Area of Science:

• Nephrology
• Genomics
• Biomarker Discovery

Background:

• Diabetic nephropathy (DN) is a leading cause of end-stage renal disease.
• Current diagnostic markers for DN are insufficient, necessitating new approaches.
• DN imposes a significant economic burden globally.

Purpose of the Study:

• To identify novel diagnostic biomarkers for diabetic nephropathy (DN).
• To investigate the potential of APOC1 as a diagnostic marker and therapeutic target for DN.

Main Methods:

• Differential gene expression (DEG) analysis and Weighted Gene Co-expression Network Analysis (WGCNA).
• Screening of core secreted genes using Lasso and SVM-RFE algorithms.
• Validation of hub gene expression in DN mouse models and human clinical specimens using WB, IHC, IF, and Elias assays.

Main Results:

• 17 hub secretory genes were identified, with 6 selected by Lasso and SVM-RFE.
• APOC1 expression was significantly elevated in DN mouse renal tissue and human serum.
• APOC1 levels correlated with proteinuria and GFR in DN patients, demonstrating high diagnostic accuracy (AUC=92.5%).

Conclusions:

• APOC1 is identified as a novel diagnostic biomarker for diabetic nephropathy.
• APOC1 shows potential as a candidate for therapeutic intervention in DN.