Blood transcriptome changes after stroke in an African American population.

Area of Science:

• Genomics
• Molecular Diagnostics
• Bioinformatics

Background:

• Acute stroke diagnosis and prognosis require improved tools.
• Microarray gene expression studies suggest peripheral blood analysis is viable.
• Next-generation sequencing (NGS) offers broader genomic analysis than microarrays.

Purpose of the Study:

• To conduct a pilot study using NGS to identify gene and exon expression patterns for stroke diagnosis and prognosis.
• To explore the utility of exon expression patterns over traditional gene expression for prediction models.

Main Methods:

• Recruited 28 stroke patients and 28 controls.
• Extracted RNA from blood samples for RNA-Seq library preparation and sequencing.
• Focused analysis on 17 patients with middle cerebral artery occlusion ischemic stroke.

Main Results:

• Bioinformatic analysis revealed significant exonic, intronic, and novel RNA components.
• Utilized exonic RNA expression, rather than combined gene expression, for prediction models.
• Support vector machine models achieved >90% accuracy for diagnosis (88% specificity, 92% sensitivity).
• Achieved 100% accuracy in predicting patient outcome based on NIH Stroke Scale improvement.

Conclusions:

• NGS-based exon expression analysis demonstrates high potential for accurate patient diagnosis and outcome prediction.
• This approach has clear utility in aiding clinical patient care for acute stroke.