Comparison of molecular mechanisms of rheumatoid arthritis and osteoarthritis using gene microarrays

Abstract

The present study aimed to compare the molecular mechanisms of rheumatoid arthritis (RA) and osteoarthritis (OA). The microarray dataset no. GSE29746 was downloaded from Gene Expression Omnibus. After data pre‑processing, differential expression analysis between the RA group and the control, as well as between the OA group and the control was performed using the LIMMA package in R and differentially expressed transcripts (DETs) with |log2fold change (FC)|>1 and P<0.01 were identified. DETs screened from each disease group were then subjected to functional annotation using DAVID. Next, DETs from each group were used to construct individual interaction networks using the BIND database, followed by sub‑network mining using clusterONE. Significant functions of nodes in each sub‑network were also investigated. In total, 19 and 281 DETs were screened from the RA and OA groups, respectively, with only six common DETs. DETs from the RA and OA groups were enriched in 8 and 130 gene ontology (GO) terms, respectively, with four common GO terms, of which to were associated with phospholipase C (PLC) activity. In addition, DETs screened from the OA group were enriched in immune response‑associated GO terms, and those screened from the RA group were largely associated with biological processes linked with the cell cycle and chromosomes. Genes involved in PLC activity and its regulation were indicated to be altered in RA as well as in OA. Alterations in the expression of cell cycle‑associated genes were indicated to be linked with the occurrence of OA, while genes participating in the immune response were involved in the occurrence of RA.

Figure 1

Volcano plots of expression values of all the transcripts. RA, synovial fibroblasts from rheumatoid arthritis group; OA, synovial fibroblasts from the osteoarthritis group; HSF, healthy synovial fibroblasts; DEG, differentially expressed gene.

Figure 2

Venn diagram of differentially expressed transcripts between the rheumatoid arthritis group and the osteoarthritis group. RA, synovial fibroblasts from rheumatoid arthritis group; OA, synovial fibroblasts from the osteoarthritis group.

Figure 3

Clustering analysis of samples based on the screened differentially expressed transcripts. (A) Clustering analysis was based on the expression levels of 19 transcripts differentially expressed between the RA group and the healthy controls. (B) Clustering analysis based on the expression levels of 281 transcripts differentially expressed between the synovial fibroblasts in the OA group and the healthy controls. Each row represents a single gene and each column represents a tissue sample. The colored bar at the top indicates the group: Gray, HSF; blue, synovial fibroblasts from RA group; pink, synovial fibroblasts from OA group. Expression levels were indicated in a heat-map style with dark red indicating high expression and dark green low expression. RA, rheumatoid arthritis; OA, osteoarthritis; HSF, healthy synovial fibroblasts.

Figure 4

Interaction network of differentially expressed transcripts screened from synovial fibroblasts of patients with rheumatoid arthritis. Green squares represent downregulated DEGs and red squares indicate upregulated DEGs. DEG, differentially expressed gene.

Figure 5

Interaction network of DEGs screened from synovial fibroblasts from patients in osteoarthritis group. Green squares represent downregulated DEGs and red squares indicate upregulated DEGs. DEG, differentially expressed gene.