BACKGROUND: Essential thrombocythemia (ET) is a form of chronic myeloproliferative neoplasm (MPN), and thrombosis is an important complication. This study aimed to use bioinformatics analysis to identify differentially expressed genes (DEGs) in ET associated thrombosis.

MATERIAL AND METHODS: Two datasets were identified from the Gene Expression Omnibus (GEO) database to investigate the expression profiles in ET. The GSE103176 dataset included 24 patients with ET and 15 healthy individuals with samples from CD34+ bone marrow cells. The GSE54644 dataset included 47 patients with ET and 11 healthy individuals with samples from peripheral neutrophils. GEO2R was used to screen DEGs, followed by over-representation analysis. Protein–protein interaction (PPI) network analysis and module analysis were performed using the STRING database and Cytoscape software. Hub genes were identified using the cytoHubba Cytoscape plugin, and maximal clique centrality (MCC) was identified. The MCODE Cytoscape plugin was used to identify network clusters, or highly interconnected regions.

RESULTS: There were 586 and 392 DEGs identified from the GSE103176 and GSE54644 datasets, respectively. The upregulated DEGs for CD34+ cells were predominantly enriched for granulocyte activation or related pathways for biological process (BP), and secretory vesicle for the cellular component (CC). The top hub genes within CD34+ cells included CXCL1, CAMP, HP, MMP8, PTX3, ORM1, LYZ, LTF, PGLYRP1, and OLFM4.

CONCLUSIONS: Bioinformatics analysis identified DEGs and hub genes that interacted with CD34+ cells and neutrophils that may predict an increased risk of thrombosis in patients with ET. These preliminary findings should be validated using next-generation sequencing (NGS) and clinical studies.

Keywords: Neutrophil Activation, Secretory Vesicles, Thrombocythemia, Essential, Thrombosis, Biomarkers, Tumor, Computational Biology, Databases, Genetic, Gene Expression Profiling, gene ontology, Gene Regulatory Networks, High-Throughput Nucleotide Sequencing, Liver Neoplasms, Protein Interaction Maps, Software