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

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.