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Gene expression microarray data identify hub genes involved in Osteoarthritis

Citation

Zhou, J and Zou, D and Wan, R and Liu, J and Zhou, Q and Zhou, Z and Wang, W and Tao, C and Liu, T, Gene expression microarray data identify hub genes involved in Osteoarthritis, Frontiers in Genetics, 13 ISSN 1664-8021 (2022) [Refereed Article]


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DOI: doi:10.3389/fgene.2022.870590

Abstract

The present study was performed to explore the underlying molecular mechanisms and screen hub genes of osteoarthritis (OA) via bioinformatics analysis. In total, twenty-five OA synovial tissue samples and 25 normal synovial tissue samples were derived from three datasets, namely, GSE55457, GSE55235, and GSE1919, and were used to identify the differentially expressed genes (DEGs) of OA by R language. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of DEGs were conducted using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). A Venn diagram was built to show the potential hub genes identified in all three datasets. The STRING database was used for constructing the protein–protein interaction (PPI) networks and submodules of DEGs. We identified 507 upregulated and 620 downregulated genes. Upregulated DEGs were significantly involved in immune response, MHC class II receptor activity, and presented in the extracellular region, while downregulated DEGs were mainly enriched in response to organic substances, extracellular region parts, and cadmium ion binding. Results of KEGG analysis indicated that the upregulated DEGs mainly existed in cell adhesion molecules (CAMs), while downregulated DEGs were significantly involved in the MAPK signaling pathway. A total of eighteen intersection genes were identified across the three datasets. These include Nell-1, ATF3, RhoB, STC1, and VEGFA. In addition, 10 hub genes including CXCL12, CXCL8, CCL20, and CCL4 were found in the PPI network and module construction. Identification of DEGs and hub genes associated with OA may be helpful for revealing the molecular mechanisms of OA and further promotes the development of relevant biomarkers and drug targets.

Item Details

Item Type:Refereed Article
Keywords:osteoarthritis, bioinformatics analysis, enrichment analysis, PPI network, microarray
Research Division:Biological Sciences
Research Group:Bioinformatics and computational biology
Research Field:Translational and applied bioinformatics
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the biomedical and clinical sciences
UTAS Author:Zhou, Z (Dr Zhen Zhou)
ID Code:150533
Year Published:2022
Deposited By:Menzies Institute for Medical Research
Deposited On:2022-06-19
Last Modified:2022-06-22
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