Abstract TP259: Discovery of Stroke-Related Blood Biomarkers From Gene Expression Network Models

Abstract

Introduction: Identifying the molecular biomarkers characteristic of ischemic stroke has the potential to aid in distinguishing stroke cases from stroke symptom mimics while advancing the understanding of the physiological changes underlying the body's response to stroke. Hypothesis: Measures of network centrality can identify a more statistically robust and functionally significant set of molecular biomarkers to distinguish between stroke and non-stroke cases than methods based solely on the absolute magnitudes of gene expression fold-change. Methods: Mutual information values for the expression levels of 13243 quantified transcripts were evaluated in blood samples from 128 stroke patients and 67 non-stroke patients to construct co-expression networks of genes. Page rank centrality scores were computed for every gene; a gene’s significance in the network was assessed according to the differences in their network centrality between stroke and non-stroke expression patterns. A hybrid genetic algorithm-support vector machine learning tool (InSyBio) was used to classify samples based on gene centrality in order to identify an optimal set of predictor genes for stroke vs. non-stroke while minimizing the number of genes in the model. Results: A predictive model for stroke with 85.7% accuracy was found using 4 network-central genes ( AGTPBP1, SRPK1, ANTXR2, MRPL41 ). In contrast, classification models based solely on individual genes identified by significant fold-change in expression level provided predictive accuracies < 70% for any single gene. Models with much larger (10-26) numbers of gene transcript biomarkers gave lower predictive accuracies (≤ 80%) than the 4 network-based gene classification in this study. The 4 genes identified from network centrality were also significantly associated with differential expression in 6 miRNAs, and were involved in key pathways including metal ion binding, translation, and transport across cell/organelle membranes. Conclusions: This study provides an improved set of biomarkers that may be clinically useful for diagnosing stroke. Additionally, the set of co-expressed genes identified in this study provides insight into novel pathways that are activated or disrupted as a consequence of stroke.