High-throughput DeepPRM-Stellar proteomics coupled with machine learning enables precise quantification of atherosclerosis-stroke progression biomarkers and risk prediction.

Journal: The Analyst
Published Date:

Abstract

Predicting the progression of asymptomatic large-artery atherosclerosis (LAA) to acute ischemic stroke (AIS) remains a significant challenge when relying solely on anatomical stenosis. To address this clinical gap, we integrated discovery-phase serum proteomics with machine-learning techniques to identify circulating biomarkers capable of predicting atherosclerotic progression. Utilizing a dual-cohort design (Cohort I: discovery stage, = 43; Cohort II: validation stage, = 39), we established a Serum Protein Candidate Biomarker Bank (SPCBB) encompassing 1484 proteins by harmonizing literature-derived evidence (1369 proteins) with 222 differentially expressed proteins (DEPs) identified through mass spectrometry analysis. Global proteomics revealed that LAA-associated proteins were enriched in cholesterol metabolism, whereas AIS was characterized by the activation of complement/coagulation cascades. We performed targeted validation of 171 peptides (corresponding to 156 proteins) using DeepPRM on the Stellar platform, thereby facilitating machine learning-based optimization of the biomarker panel. The XGBoost algorithm identified two diagnostic signatures: a three-protein panel (RNASE4, HBA1, ATF6B) that differentiates AIS from LAA, with an area under the curve (AUC) of 0.917 and specificity of 80.0%; and a six-protein panel (MRC1, HBA1, GUC2A, HBD, CLEC3B, FLNA) that distinguishes AIS/LAA from healthy controls, achieving an AUC of 0.971 and specificity of 86.0%. To further validate key candidates, we performed ELISA assays for GUCA2A and FLNA, which confirmed their significant elevation in patients with AIS and LAA ( < 0.01), consistent with the proteomics findings. Both internal and external validations confirmed robust performance across cohorts. These validated biomarker panels establish a proteomics-driven framework for serum-based, dynamic monitoring of LAA progression, thereby supporting clinical decision-making aimed at optimizing early stroke prevention in asymptomatic individuals.

Authors

  • Ye Liu
    Department of Cell Biology, Van Andel Research Institute, 333 Bostwick Ave NE, Grand Rapids, MI, 49503, USA.
  • Ouyang Hu
    State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Fujian 361102, China. ytxiong@xmu.edu.cn.
  • Zhenxin Wang
    State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences Changchun 130022 P. R. China wangzx@ciac.ac.cn zhuqy@ciac.ac.cn.
  • Jingyi Wang
  • Yijie Qiu
    State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Fujian 361102, China. ytxiong@xmu.edu.cn.
  • Jin Xiao
    Sichuan University, China.
  • Xin Cheng
    International Joint Laboratory for Embryonic Development & Prenatal Medicine Division of Histology and Embryology School of Medicine Jinan University Guangzhou China.
  • Pengyuan Yang
    Department of Chemistry, Shanghai Stomatological Hospital, and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200000, China.
  • Ningshao Xia
  • Yueting Xiong
    State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Fujian 361102, China. ytxiong@xmu.edu.cn.
  • Quan Yuan
    School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China.

Keywords

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