Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis

Abstract

Background: The disability rate associated with rheumatoid arthritis (RA) ranks high among inflammatory joint diseases. However, the cause and potential molecular events are as yet not clear. Here, we aimed to identify differentially expressed genes (DEGs), pathways and immune infiltration involved in RA utilizing integrated bioinformatics analysis and investigating potential molecular mechanisms.

Materials and methods: The expression profiles of GSE55235, GSE55457, GSE55584 and GSE77298 were downloaded from the Gene Expression Omnibus database, which contained 76 synovial membrane samples, including 49 RA samples and 27 normal controls. The microarray datasets were consolidated and DEGs were acquired and further analyzed by bioinformatics techniques. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using R (version 3.6.1) software, respectively. The protein-protein interaction (PPI) network of DEGs were developed utilizing the STRING database. Finally, the CIBERSORT was used to evaluate the infiltration of immune cells in RA.

Results: A total of 828 DEGs were recognized, with 758 up-regulated and 70 down-regulated. GO and KEGG pathway analyses demonstrated that these DEGs focused primarily on cytokine receptor activity and relevant signaling pathways. The 30 most firmly related genes among DEGs were identified from the PPI network. The principal component analysis showed that there was a significant difference between the two tissues in infiltration immune.

Conclusion: This study shows that screening for DEGs, pathways and immune infiltration utilizing integrated bioinformatics analyses could aid in the comprehension of the molecular mechanisms involved in RA development. Besides, our study provides valuable data related to DEGs, pathways and immune infiltration of RA and may provide new insight into the understanding of molecular mechanisms.

Keywords: Bioinformatics analysis; Differentially expressed genes; Immune infiltration; Rheumatoid arthritis.

Fig. 1

Serials selection process

Fig. 2

Volcano plot of the differentially expressed genes between RA and normal synovial tissues. Black points represent the adjusted P-value>0.05. Green points represent adjusted P-value<0.05 and down-regulated genes. Red points represent adjusted P-value<0.05 and the up-regulated genes

Fig. 3

Heatmap of the top 100 DEGs according to the adjusted P-value and logFC. Red indicates higher gene expression and green indicates lower gene expression

Fig. 4

GO and KEGG pathway enrichment analysis of DEGs in GSE55235, GSE55457, GSE55584 and GSE77298. a GO terms in the enrichment analysis of the up-regulated genes. b GO terms in the enrichment analysis of the down-regulated genes. c KEGG terms in the enrichment analysis of the up-regulated genes. d KEGG terms in the enrichment analysis of the down-regulated genes

Fig. 5

KEGG pathway enrichment analysis and pathway map for RA

Fig. 6

a PPI network (828 DEGs filtered into the PPI network that contained 103 nodes and 168 edges). b The predicted association rank (from low to high) of the top 30 genes in the PPI network

Fig. 7

Results of CIBERSORT analysis of Gene Expression Omnibus database. a Principal component analysis (PCA) was performed on two groups. Red points and ellipse indicate RA sample, and green points and ellipse indicate normal samples. b Correlation matrix of infiltration degree of immune cells in RA samples. Red indicates trends consistent with the positive correlation and blue indicates trends consistent with the negative correlation between two immune cells. The bigger size of the numbers statistics data represents the more positive or negative correlation. c Landscape of immune cell infiltration

Fig. 8

The landscape of immune infiltration between RA and normal controls. a The distribution of 22 immune cells in 69 filtered gene matrix. Red indicates higher immune infiltration expression and green indicates lower expression. b Violin diagram of immune cell proportions in two groups. The blue fusiform fractions on the left represent the normal group and the red fusiform fractions on the right represent the RA group.