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Published on: August 11, 2015
Circulating proteomic panels for risk stratification of intracranial aneurysm and its rupture
Yueting Xiong1, Yongtao Zheng2, Yan Yan3
1The Fifth People's Hospital of Shanghai, Shanghai Key Laboratory of Medical Epigenetics, The International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
Insights
Researchers identified novel blood biomarkers to diagnose intracranial aneurysms (IA) and predict rupture. This discovery aids in developing new treatments for this serious condition.
Area of Science:
- Biomarker Discovery
- Proteomics
- Machine Learning
Background:
- Intracranial aneurysm (IA) prevalence is rising, with severe consequences upon rupture.
- Accurate diagnosis and classification of IA are crucial for effective treatment strategies.
Purpose of the Study:
- To identify specific, sensitive, and non-invasive biomarkers for diagnosing and classifying ruptured and unruptured IA.
- To facilitate the development of novel therapeutics for IA.
Main Methods:
- Assembled a candidate biomarker bank of 717 proteins from proteomic analysis and prior studies.
- Developed DeepPRM, a deep learning method for efficient mass spectrometry assay design.
- Quantitated 113 potential markers in serum cohorts (n=212 & n=32) and applied a machine-learning pipeline.
Main Results:
- Achieved 87.50% accuracy in distinguishing IA from healthy controls using biomarker combinations (P6 & P8).
- Attained 91.67% accuracy in classifying IA rupture status in an external validation set (n=32).
- Demonstrated the potential of circulating biomarkers for IA management.
Conclusions:
- This study presents a valuable set of circulating biomarkers for IA diagnosis and rupture classification.
- The findings support the development of non-invasive diagnostic tools and targeted therapies for IA.
- DeepPRM and machine learning offer efficient approaches for biomarker discovery in complex diseases.
Abstract:
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.
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