Deborah A Lewis1, Sunil Suchindran2, Michele G Beckman3, W Craig Hooper3, Althea M Grant3, John A Heit4, Marilyn Manco-Johnson5, Stephan Moll6, Claire S Philipp7, Kristy Kenney3, Christine De Staercke3, Meredith E Pyle3, Jen-Tsan Chi8, Thomas L Ortel9. 1. Thrombosis and Hemostasis Center, Division of Hematology, Duke University Medical Center, Durham, NC. 2. Center for Applied Genomics, Duke University School of Medicine, Durham NC. 3. Centers for Disease Control and Prevention, Atlanta, GA; Division of Cardiovascular Diseases. 4. Mayo Clinic, Rochester, MN. 5. University of Colorado and Children's Hospital, Aurora, CO. 6. University of North Carolina, Chapel Hill, NC. 7. Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ. 8. Department of Molecular Genetics and Microbiology and Center for Genomic and Computation Biology, Duke University Medical Center, Durham NC. 9. Thrombosis and Hemostasis Center, Division of Hematology, Duke University Medical Center, Durham, NC. Electronic address: thomas.ortel@duke.edu.
Abstract
INTRODUCTION: Recurrent venous thromboembolism (VTE) occurs infrequently following a provoked event but occurs in up to 30% of individuals following an initial unprovoked event. There is limited understanding of the biological mechanisms that predispose patients to recurrent VTE. OBJECTIVES: To identify whole blood gene expression profiles that distinguished patients with clinically distinct patterns of VTE. PATIENTS/ METHODS: We studied 107 patients with VTE separated into 3 groups: (1) 'low-risk' patients had one or more provoked VTE; (2) 'moderate-risk' patients had a single unprovoked VTE; (3) 'high-risk' patients had ≥2 unprovoked VTE. Each patient group was also compared to twenty-five individuals with no personal history of VTE. Total RNA from whole blood was isolated and hybridized to Illumina HT-12V4 Beadchips to assay whole genome expression. RESULTS: Using class prediction analysis, we distinguished high-risk patients from low-risk patients and healthy controls with good receiver operating curve characteristics (AUC=0.81 and 0.84, respectively). We also distinguished moderate-risk individuals and low-risk individuals from healthy controls with AUC's of 0.69 and 0.80, respectively. Using differential expression analysis, we identified several genes previously implicated in thrombotic disorders by genetic analyses, including SELP, KLKB1, ANXA5, and CD46. Protein levels for several of the identified genes were not significantly different between the different groups. CONCLUSION: Gene expression profiles are capable of distinguishing patients with different clinical presentations of VTE, and genes relevant to VTE risk are frequently differentially expressed in these comparisons.
INTRODUCTION: Recurrent venous thromboembolism (VTE) occurs infrequently following a provoked event but occurs in up to 30% of individuals following an initial unprovoked event. There is limited understanding of the biological mechanisms that predispose patients to recurrent VTE. OBJECTIVES: To identify whole blood gene expression profiles that distinguished patients with clinically distinct patterns of VTE. PATIENTS/ METHODS: We studied 107 patients with VTE separated into 3 groups: (1) 'low-risk' patients had one or more provoked VTE; (2) 'moderate-risk' patients had a single unprovoked VTE; (3) 'high-risk' patients had ≥2 unprovoked VTE. Each patient group was also compared to twenty-five individuals with no personal history of VTE. Total RNA from whole blood was isolated and hybridized to Illumina HT-12V4 Beadchips to assay whole genome expression. RESULTS: Using class prediction analysis, we distinguished high-risk patients from low-risk patients and healthy controls with good receiver operating curve characteristics (AUC=0.81 and 0.84, respectively). We also distinguished moderate-risk individuals and low-risk individuals from healthy controls with AUC's of 0.69 and 0.80, respectively. Using differential expression analysis, we identified several genes previously implicated in thrombotic disorders by genetic analyses, including SELP, KLKB1, ANXA5, and CD46. Protein levels for several of the identified genes were not significantly different between the different groups. CONCLUSION: Gene expression profiles are capable of distinguishing patients with different clinical presentations of VTE, and genes relevant to VTE risk are frequently differentially expressed in these comparisons.
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