» Articles » PMID: 29620629

Identification of Biomarkers of Venous Thromboembolism by Bioinformatics Analyses

Overview
Specialty General Medicine
Date 2018 Apr 6
PMID 29620629
Citations 5
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

Exploration of the causal relationship and mechanisms between serum albumin and venous thrombosis: a bidirectional mendelian randomization analysis and bioinformatics study.

Xia X, Tie X, Hong M, Yin W Thromb J. 2025; 23(1):17.

PMID: 40033322 PMC: 11874119. DOI: 10.1186/s12959-025-00700-4.


Bioinformatics-based discovery of biomarkers and immunoinflammatory infiltrates in hip fractures complicating deep vein thrombosis: A STROBE.

Fu Z, Song C, Mei Y, Zhou D, Zhou Y, Chen J Medicine (Baltimore). 2025; 103(52):e40809.

PMID: 39969342 PMC: 11688061. DOI: 10.1097/MD.0000000000040809.


CREB1 Silencing Protects Against Inflammatory Response in Rats with Deep Vein Thrombosis Through Reducing RPL9 Expression and Blocking NF-κB Signaling.

Jian X, Yang D, Wang L, Wang H J Cardiovasc Transl Res. 2023; 17(3):570-584.

PMID: 37891366 DOI: 10.1007/s12265-023-10450-1.


Deep sequencing of circulating miRNAs and target mRNAs level in deep venous thrombosis patients.

Wang Q, Chang Y, Yang X, Han Z IET Syst Biol. 2023; 17(4):212-227.

PMID: 37466160 PMC: 10439493. DOI: 10.1049/syb2.12071.


Identification of four hub genes in venous thromboembolism via weighted gene coexpression network analysis.

Fan G, Jin Z, Wang K, Yang H, Wang J, Li Y BMC Cardiovasc Disord. 2021; 21(1):577.

PMID: 34861826 PMC: 8642897. DOI: 10.1186/s12872-021-02409-4.


References
1.
Kwon A, Jo S, Jo Y, Park J, Kim M, Kang H . Genetic polymorphisms and plasma levels of tissue factor and tissue factor pathway inhibitor in venous thromboembolism. Blood Coagul Fibrinolysis. 2014; 25(5):416-21. DOI: 10.1097/MBC.0000000000000063. View

2.
Prandoni P, Falanga A, Piccioli A . Cancer and venous thromboembolism. Lancet Oncol. 2005; 6(6):401-10. DOI: 10.1016/S1470-2045(05)70207-2. View

3.
Shannon P, Markiel A, Ozier O, Baliga N, Wang J, Ramage D . Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003; 13(11):2498-504. PMC: 403769. DOI: 10.1101/gr.1239303. View

4.
Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P . The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 2010; 39(Database issue):D561-8. PMC: 3013807. DOI: 10.1093/nar/gkq973. View

5.
Fritz J, Ferrero R, Philpott D, Girardin S . Nod-like proteins in immunity, inflammation and disease. Nat Immunol. 2006; 7(12):1250-7. DOI: 10.1038/ni1412. View