Cerebral small vessel disease (CSVD) is a global brain disorder that is characterized by a series of clinical, neuroimaging, and neuropathological manifestations. However, the molecular pathophysiological mechanisms of CSVD have not been thoroughly investigated. Liquid chromatography-tandem mass spectrometry-based proteomics has broad application prospects in biomedicine. It is used to elucidate disease-related molecular processes and pathophysiological pathways, thus providing an important opportunity to explore the pathophysiological mechanisms of CSVD. Serum samples were obtained from 96 participants (58 with CSVD and 38 controls) consecutively recruited from The First Affiliated Hospital of Zhengzhou University. After removing high-abundance proteins, the serum samples were analyzed using high-resolution mass spectrometry. Bioinformatics methods were used for in-depth analysis of the obtained proteomic data, and the results were verified experimentally. Compared with the control group, 52 proteins were differentially expressed in the sera of the CSVD group. Furthermore, analyses indicated the involvement of these differentially expressed proteins in CSVD through participation in the overactivation of complement and coagulation cascades and dysregulation of insulin-like growth factor-binding proteins. The proteomic biomarker panel identified by the machine learning model combined with clinical features is expected to facilitate the diagnosis of CSVD (AUCâ=â0.947, 95% CIâ=â0.895-0.978). The study is the most in-depth study on CSVD proteomics to date and suggests that the overactivation of the complement cascade and the dysregulation of IGFBP on- IGF may be closely correlated with the occurrence and progression of CSVD, offering the potential to develop peripheral blood biomarkers and providing new insights into the biological basis of CSVD.
Proteome Profiling of Serum Reveals Pathological Mechanisms and Biomarker Candidates for Cerebral Small Vessel Disease.
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作者:Wang Yun-Chao, Zhu Hang-Hang, He Liu-Chang, Yao Ya-Ting, Zhang Lei, Xue Xin-Li, Li Jing-Yi, Zhang Li, Song Bo, Shi Chang-He, Li Yu-Sheng, Gao Yuan, Yang Jing-Hua, Xu Yu-Ming
| 期刊: | Translational Stroke Research | 影响因子: | 4.300 |
| 时间: | 2025 | 起止号: | 2025 Oct;16(5):1606-1620 |
| doi: | 10.1007/s12975-025-01332-6 | ||
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