ArticleThrombosis research2015
Whole blood gene expression profiles distinguish clinical phenotypes of venous thromboembolism.
Article in Thrombosis research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A Mitochondrial-Related Gene Signature for Diagnosis and Immune Microenvironment Modulation in Lung Cancer and Venous Thromboembolism.World journal of oncology · 2026Article
- Development of Venous Thromboembolism Risk Prediction Models Based on Whole Blood Gene Expression Profiling Using 20 Machine Learning Algorithms: Comprehensive Analysis Study.JMIR medical informatics · 2026Article
- Multi-Omics Prioritization and Clinical Evaluation of CYP4V2 as a Candidate Biomarker in Venous Thromboembolism.International journal of general medicine · 2026Article
- Investigating associations between JAK inhibition and venous thromboembolism by systematic mining of large-scale datasets.Inflammopharmacology · 2025Article
- Uncovering novel therapeutic clues for hypercoagulable active ulcerative colitis: novel findings from old data.Gastroenterology report · 2024Article
- The Underlying Molecular Basis and Mechanisms of Venous Thrombosis in Patients with Osteomyelitis: A Data-Driven Analysis.Genetics research · 2022Article
- Identification of four hub genes in venous thromboembolism via weighted gene coexpression network analysis.BMC cardiovascular disorders · 2021Article
- Gene Expression Signature in Patients With Symptomatic Peripheral Artery Disease.Arteriosclerosis, thrombosis, and vascular biology · 2021Article
- Dysregulated transcriptional responses to SARS-CoV-2 in the periphery support novel diagnostic approaches.medRxiv : the preprint server for health sciences · 2020Article
- Article
- New Paradigm of Machine Learning (ML) in Personalized Oncology: Data Trimming for Squeezing More Biomarkers From Clinical Datasets.Frontiers in oncology · 2019Article
- Identification of Genetic Interaction with Risk Factors Using a Time-To-Event Model.International journal of environmental research and public health · 2017Article
- Identification of unique venous thromboembolism-susceptibility variants in African-Americans.Thrombosis and haemostasis · 2017Article
- Blood-based omic profiling supports female susceptibility to tobacco smoke-induced cardiovascular diseases.Scientific reports · 2017Article
- A Novel Prioritization Method in Identifying Recurrent Venous Thromboembolism-Related Genes.PloS one · 2016Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
introductionRecurrent 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.
objectivesTo identify whole blood gene expression profiles that distinguished patients with clinically distinct patterns of VTE. PATIENTS/
methodsWe 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.
resultsUsing 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.
conclusionGene 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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