Data Management And Sharing System of China National Clinical Research Center for Neurological Diseases
Biomarker and genomic analyses reveal molecular signatures of non-cardioembolic ischemic stroke
First author：Lingling Ding
Corresponding author：Zixiao Li
essential_information
- Person in charge：Lingling Ding
- Data Sources： CNSR-Ⅲ
- Number of papers：CNSR-Ⅲ20200210
- Published time：2023-05-30
2023-05-30
Published articles
Journal： Signal Transduction and Targeted Therapy
Publication type: Article
IF(IF): 39.3
PMID: 37248226 Link To Pubmed Full text Download
Background And Purpose
Deep phenotyping will provide more specificity, new types of big data, and hidden subtypes of complex diseases. Taking a deep-phenotyping approach to ischemic stroke might break the current impasse in progress to better management. We aim to derive stroke phenotypes based on unsupervised analysis of clinical data.
Method
We integrated data from the Third China National Stroke Registry, including clinical phenotypes, biomarkers, and whole-genome sequencing data for 7695 patients with NCIS. We identified 30 molecular clusters based on 63 biomarkers and explored the comprehensive landscape of biological heterogeneity and subpopulations in NCIS. Dimensionality reduction revealed fine-scale subpopulation structures associated with specific biomarkers.
Conclusion
Our study demonstrates the power of large-scale molecular biomarkers and genomics to understand the underlying biological mechanisms of and advance precision medicine for NCIS.
Main results
The subpopulations with biomarkers for inflammation, abnormal liver and kidney function, homocysteine metabolism, lipid metabolism, and gut microbiota metabolism were associated with a high risk of unfavorable clinical outcomes, including stroke recurrence, disability, and mortality. Several genes encoding potential drug targets were identified as putative causal genes that drive the clusters, such as CDK10, ERCC3, and CHEK2. We comprehensively characterized the genetic architecture of these subpopulations, identified their molecular signatures, and revealed the potential of the polybiomarkers and polygenic prediction for assessing clinical outcomes.