Urinary protein biomarkers based on LC-MS/MS analysis to discriminate vascular dementia from Alzheimer's disease in Han Chinese population

Ruijuan Chen; Yuanjing Yi; Wenbiao Xiao; Bowen Zhong; Le Zhang; Yi Zeng

doi:10.3389/fnagi.2023.1070854

Urinary protein biomarkers based on LC-MS/MS analysis to discriminate vascular dementia from Alzheimer's disease in Han Chinese population

Front Aging Neurosci. 2023 Jan 25:15:1070854. doi: 10.3389/fnagi.2023.1070854. eCollection 2023.

Authors

Ruijuan Chen^{1

2}, Yuanjing Yi¹, Wenbiao Xiao¹, Bowen Zhong³, Le Zhang⁴, Yi Zeng¹

Affiliations

¹ Department of Geriatrics, Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
² Department of Emergency, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou, Hainan, China.
³ State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China.
⁴ Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, China.

Abstract

Objective: This study aimed to identify the potential urine biomarkers of vascular dementia (VD) and unravel the disease-associated mechanisms by applying Liquid chromatography tandem-mass spectrometry (LC-MS/MS).

Methods: LC-MS/MS proteomic analysis was applied to urine samples from 3 groups, including 14 patients with VD, 9 patients with AD, and 21 normal controls (NC). By searching the MS data by Proteome Discoverer software, analyzing the protein abundances qualitatively and quantitatively, comparing between groups, combining bioinformatics analysis using Gene Ontology (GO) and pathway crosstalk analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG), and literature searching, the differentially expressed proteins (DEPs) of VD can be comprehensively determined at last and were further quantified by receiver operating characteristic (ROC) curve methods.

Results: The proteomic findings showed quantitative changes in patients with VD compared to patients with NC and AD groups; among 4,699 identified urine proteins, 939 and 1,147 proteins displayed quantitative changes unique to VD vs. NC and AD, respectively, including 484 overlapped common DEPs. Then, 10 unique proteins named in KEGG database (including PLOD3, SDCBP, SRC, GPRC5B, TSG101/STP22/VPS23, THY1/CD90, PLCD, CDH16, NARS/asnS, AGRN) were confirmed by a ROC curve method.

Conclusion: Our results suggested that urine proteins enable detection of VD from AD and VC, which may provide an opportunity for intervention.

Keywords: Alzheimer’s disease; biomarkers; proteomics; urine; vascular dementia.