Table 4 The summary of all important biomarkers discovered in this study.

From: Quantitative proteomics and phosphoproteomics of urinary extracellular vesicles define putative diagnostic biosignatures for Parkinson’s disease

Type of biomarkers

Putative biomarkers

Visualized data

Biomarkers of LRRK2 PD vs. control

RAB2A, RAB10, pRAB12

Supplementary Fig. 12a

Biomarkers of LRRK2 PD vs. NMC

PRDX3, KLK6, TRIM17, TPT1, VCAM1, LILRB1, IGF1, PCSK1N, STK11

Fig. 3a

Biomarkers of LRRK2 PD vs. iPD

PRDX3, KLK6, TRIM17, TPT1, VCAM1, LILRB1, HSPA1A, HSPA1B, ECM1, GBA, NEDD4L, GDPD3

Fig. 3b

Biomarkers with strong correlation with UPDRS-III

PEBP4, NEDD4L, KLK6

Fig. 4

Top disease biomarkers chosen by machine learning

PCSK1N, HNRNPA1, pPLA2G4A, pLTB4R, pPRR15, pPPFIA1

Fig. 5

Disease biomarkers validated using PRM-MS

HNRNPA1, HNF4A, FN1, APP, APOM, STK11, CD9, CD63, CD81

Fig. 7

Disease biomarkers validated using WB

HNRNPA1, PCSK1N, STK11

Supplementary Fig. 10a, b

Biomarkers expressed significantly higher in males

ENPEP, GDPD3, NAGA, NEDD4L, QPRT, SCAMP3, RAB1B, RAB7A, RAB3D

Supplementary Figs. 6a and 12b

Biomarkers expressed significantly higher in females

RAB1A

Supplementary Fig. 12b

  1. The listed putative biomarkers were discovered using various approaches, such as Pearson correlation analyses, machine learning, parallel reaction monitoring (PRM-MS), Western blot (WB), and statistical analyses of the protein or phosphoprotein expressions.
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