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Identification of diagnostic signatures for ischemic stroke by machine learning algorithm.

Qian Li1, Yu Tian2, Jingyan Niu3

  • 1Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province 150081, China.

Journal of Stroke and Cerebrovascular Diseases : the Official Journal of National Stroke Association
|January 12, 2024
PubMed
Summary

This study identifies four key genes (LAMP2, CR1, CLEC4E, F5) as diagnostic markers for ischemic stroke (IS). These findings offer new insights into the role of immune cell infiltration in IS development.

Keywords:
BiomarkerCIBERSORTCardioembolic strokeImmune cell infiltrationIschemic stroke

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Area of Science:

  • Neuroscience
  • Immunology
  • Genetics

Background:

  • Ischemic stroke (IS) is a primary cause of mortality and disability.
  • Understanding IS pathogenesis, particularly immune cell involvement, is crucial.

Purpose of the Study:

  • To identify diagnostic gene signatures for IS.
  • To explore the role of immune cell infiltration in IS pathogenesis.

Main Methods:

  • Integrated bioinformatics analysis of microarray data from IS patients.
  • Machine learning strategies (Lasso, SVM-RFE) for signature selection.
  • Weighted Gene Co-expression Network Analysis (WGCNA) and CIBERSORT for immune cell infiltration analysis.

Main Results:

  • Four diagnostic genes (LAMP2, CR1, CLEC4E, F5) were identified with high predictive accuracy (AUC 0.954 training, 0.862 validation).
  • Immune response pathways and pattern recognition receptor activity were enriched in IS.
  • Significant correlations were found between diagnostic genes and various immune cells, including plasma cells, NK cells, dendritic cells, B cells, and T cells.

Conclusions:

  • LAMP2, CR1, CLEC4E, and F5 serve as potential diagnostic biomarkers for IS.
  • The study highlights the intricate relationship between immune cell infiltration and IS molecular mechanisms.