Identification of key genes associated with rheumatoid arthritis with bioinformatics approach

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Abstract

We aimed to identify key genes associated with rheumatoid arthritis (RA).

The microarray datasets of GSE1919, GSE12021, and GSE21959 (35 RA samples and 32 normal controls) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) in RA samples were identified using the t test in limma package. Functional enrichment analysis was performed using clusterProfiler package. A protein–protein interaction (PPI) network of selected DEGs was constructed based on the Human Protein Reference Database. Active modules were explored using the jActiveModules plug-in in the Cytoscape Network Modeling package.

In total, 537 DEGs in RA samples were identified, including 241 upregulated and 296 downregulated genes. A total of 24,451 PPI pairs were collected, and 5 active modules were screened. Furthermore, 19 submodules were acquired from the 5 active modules. Discs large homolog 1 (DLG1) and related DEGs such as guanylate cyclase 1, soluble, alpha 2 (GUCY1A2), N-methyl d-aspartate receptor 2A subunit (GRIN2A), and potassium voltage-gated channel member 1 (KCNA1) were identified in 8 submodules. Plasminogen (PLG) and related DEGs such as chemokine (C-X-C motif) ligand 2 (CXCL2), laminin, alpha 3 (LAMA3), complement component 7 (C7), and coagulation factor X (F10) were identified in 4 submodules.

Our results indicate that DLG1, GUCY1A2, GRIN2A, KCNA1, PLG, CXCL2, LAMA3, C7, and F10 may play key roles in the progression of RA and may serve as putative therapeutic targets for treating RA.

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