Circulating proteomic panels for risk stratification of intracranial aneurysm and its rupture

被引：22

作者：

Xiong, Yueting ^{[1
]}

Zheng, Yongtao ^{[2
]}

Yan, Yan ^{[3
]}

Yao, Jun ^{[1
]}

Liu, Hebin ^{[4
]}

Shen, Fenglin ^{[1
]}

Kong, Siyuan ^{[1
]}

Yang, Shuang ^{[1
]}

Yan, Guoquan ^{[1
]}

Zhao, Huanhuan ^{[1
]}

Zhou, Xinwen ^{[1
]}

Hu, Jia ^{[3
]}

Zhou, Bin ^{[3
]}

Jin, Tao ^{[3
]}

Shen, Huali ^{[1
]}

Leng, Bing ^{[3
]}

Yang, Pengyuan ^{[1
]}

Liu, Xiaohui ^{[1
]}

机构：

[1] Fudan Univ, Peoples Hosp Shanghai 5, Inst Biomed Sci,Minist Sci & Technol, Shanghai Key Lab Med Epigenet,Int Colab Med Epige, Shanghai, Peoples R China

[2] Shanghai Jiao Tong Univ, Ruijin Hosp, Sch Med, Dept Neurosurg, Shanghai, Peoples R China

[3] Fudan Univ, Huashan Hosp, Shanghai, Peoples R China

[4] Shanghai Omicsolut Co Ltd, Shanghai, Peoples R China

来源：

EMBO MOLECULAR MEDICINE | 2022年 / 14卷 / 02期

基金：

中国国家自然科学基金; 国家重点研发计划;

关键词：

biomarker discovery; intracranial aneurysm; mass spectrometry; serum proteome profiling; PLASMA PROTEOME; MYELOPEROXIDASE; PREVALENCE; EXPRESSION; BIOMARKERS; PROTEINS; RESOURCE; DISEASE; ASSAYS; SEX;

D O I：

10.15252/emmm.202114713

中图分类号：

R-3 [医学研究方法]; R3 [基础医学];

学科分类号：

1001 ;

摘要：

The prevalence of intracranial aneurysm (IA) is increasing, and the consequences of its rupture are severe. This study aimed to reveal specific, sensitive, and non-invasive biomarkers for diagnosis and classification of ruptured and unruptured IA, to benefit the development of novel treatment strategies and therapeutics altering the course of the disease. We first assembled an extensive candidate biomarker bank of IA, comprising up to 717 proteins, based on altered proteins discovered in the current tissue and serum proteomic analysis, as well as from previous studies. Mass spectrometry assays for hundreds of biomarkers were efficiently designed using our proposed deep learning-based method, termed DeepPRM. A total of 113 potential markers were further quantitated in serum cohort I (n = 212) & II (n = 32). Combined with a machine-learning-based pipeline, we built two sets of biomarker combinations (P6 & P8) to accurately distinguish IA from healthy controls (accuracy: 87.50%) or classify IA rupture patients (accuracy: 91.67%) upon evaluation in the external validation set (n = 32). This extensive circulating biomarker development study provides valuable knowledge about IA biomarkers.

引用

页数：14

共 48 条

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