Application of Proteomics and Machine Learning Methods to Study the Pathogenesis of Diabetic Nephropathy and Screen Urinary Biomarkers

• DOI: 10.1021/acs.jproteome.4c00267
• Publication Date: 2024-07-01T10:41:46Z
• Authors: Xi Yan; Xinglai Zhang; Haiying Li; Yongdong Zou; Wei Lu; Man Zhan; Zhiyuan Liang; Hongbin Zhuang; Xiaoqian Ran; Guanwei Ma; Xixiao Lin; Hongbo Yang; Yuhan Huang; Hanghang Wang; Liming Shen
• Keywords: Proteomics; Male; Kininogens; Middle Aged; Machine Learning; Diabetes Mellitus, Type 2; Case-Control Studies; Creatinine; Humans; Albuminuria; Diabetic Nephropathies; Female; Kallikreins; Biomarkers; Serpins; Aged

Diabetic nephropathy (DN) has become the main cause of end-stage renal disease worldwide, causing significant health problems. Early diagnosis of the disease is quite inadequate. To screen urine biomarkers of DN and explore its potential mechanism, this study collected urine from 87 patients with type 2 diabetes mellitus (which will be classified into normal albuminuria, microalbuminuria, and macroalbuminuria groups) and 38 healthy subjects. Twelve individuals from each group were then randomly selected as the screening cohort for proteomics analysis and the rest as the validation cohort. The results showed that humoral immune response, complement activation, complement and coagulation cascades, renin-angiotensin system, and cell adhesion molecules were closely related to the progression of DN. Five overlapping proteins (KLK1, CSPG4, PLAU, SERPINA3, and ALB) were identified as potential biomarkers by machine learning methods. Among them, KLK1 and CSPG4 were positively correlated with the urinary albumin to creatinine ratio (UACR), and SERPINA3 was negatively correlated with the UACR, which were validated by enzyme-linked immunosorbent assay (ELISA). This study provides new insights into disease mechanisms and biomarkers for early diagnosis of DN.