Stratification of diabetic kidney diseases via data-independent acquisition proteomics-based analysis of human kidney tissue specimens

The aims of this study were to analyze the proteomic differences in renal tissues from patients with diabetes mellitus (DM) and diabetic kidney disease (DKD) and to select sensitive biomarkers for early identification of DKD progression. Pressure cycling technology-pulse data-independent acquisition mass spectrometry was employed to investigate protein alterations in 36 formalin-fixed paraffin-embedded specimens. Then, bioinformatics analysis was performed to identify important signaling pathways and key molecules. Finally, the target proteins were validated in 60 blood and 30 urine samples. A total of 52 up- and 311 down-regulated differential proteins were identified as differing among the advanced DKD samples, early DKD samples, and DM controls (adjusted p