ArticleMedicine2018
Identification of biomarkers of venous thromboembolism by bioinformatics analyses.
Article in Medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- An Immunothrombotic Extracellular Vesicle mRNA Profile Associated with Thrombosis in Lung Adenocarcinoma.International journal of molecular sciences · 2026Article
- Exploration of the causal relationship and mechanisms between serum albumin and venous thrombosis: a bidirectional mendelian randomization analysis and bioinformatics study.Thrombosis journal · 2025Article
- CREB1 Silencing Protects Against Inflammatory Response in Rats with Deep Vein Thrombosis Through Reducing RPL9 Expression and Blocking NF-κB Signaling.Journal of cardiovascular translational research · 2024Article
- Deep sequencing of circulating miRNAs and target mRNAs level in deep venous thrombosis patients.IET systems biology · 2023Article
- Identification of four hub genes in venous thromboembolism via weighted gene coexpression network analysis.BMC cardiovascular disorders · 2021Article
- Identification of genes, pathways and transcription factor-miRNA-target gene networks and experimental verification in venous thromboembolism.Scientific reports · 2021Article
- Genetic Variants of RPL5 and RPL9 Genes among Saudi Patients Diagnosed with Thrombosis.Medical archives (Sarajevo, Bosnia and Herzegovina) · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Venous thromboembolism (VTE) is a common vascular disease and a major cause of mortality. This study intended to explore the biomarkers associated with VTE by bioinformatics analyses.Based on Gene Expression Omnibus (GEO) database, the GSE19151 expression profile data were downloaded. The differentially expressed genes (DEGs) between single VTE (sVTE)/recurrent VTE (rVTE) and control were identified. Then, pathway enrichment analysis of DEGs were performed, followed by protein-protein interaction (PPI) network construction.Total 433 upregulated and 222 downregulated DEGs were obtained between sVTE and control samples. For rVTE versus control, 625 upregulated and 302 downregulated DEGs were identified. The overlap DEGs were mainly enriched in the pathways related to ribosome, cancer, and immune disease. The DEGs specific to rVTE were enriched in several pathways, such as nod-like receptor signaling pathway. In the PPI network, 2 clusters of VTE genes, including ribosomal protein family genes and NADH family-ubiquinol-cytochrome genes, were identified, such as ribosomal protein L9 (RPL9), RPL5, RPS20, RPL23, and tumor protein p53 (TP53).The nod-like receptor signaling pathway, ribosomal protein family genes, such as RPL9, RPL5, RPS20, and RPL23, and DEG of TP53 may have the potential to be used as targets for diagnosis and treatment of VTE.
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