RNA sequencing and bioinformatics analysis of differentially expressed genes in the peripheral serum of ankylosing spondylitis patients

Abstract

Background: Ankylosing spondylitis (AS) is a chronic progressive autoimmune disease characterized by spinal and sacroiliac arthritis, but its pathogenesis and genetic basis are largely unclear.

Methods: We randomly selected three serum samples each from an AS and a normal control (NC) group for high-throughput sequencing followed by using edgeR to find differentially expressed genes (DEGs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes, Reactome pathway analyses, and Gene Set Enrichment Analysis were used to comprehensively analyze the possible functions and pathways involved with these DEGs. Protein-protein interaction (PPI) networks were constructed using the STRING database and Cytoscape. The modules and hub genes of these DEGs were identified using MCODE and CytoHubba plugins. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to validate the expression levels of candidate genes in serum samples from AS patients and healthy controls.

Results: We successfully identified 100 significant DEGs in serum. When we compared them with the NC group, 49 of these genes were upregulated in AS patients and 51 were downregulated. GO function and pathway enrichment analysis indicated that these DEGs were mainly enriched in several signaling pathways associated with endoplasmic reticulum stress, including protein processing in the endoplasmic reticulum, unfolded protein response, and ubiquitin-mediated proteolysis. We also constructed a PPI network and identified the highly connected top 10 hub genes. The expression levels of the candidate hub genes PPARG, MDM2, DNA2, STUB1, UBTF, and SLC25A37 were then validated by RT-qPCR analysis. Finally, receiver operating characteristic curve analysis suggested that PPARG and MDM2 may be the potential biomarkers of AS.

Conclusions: These findings may help to further elucidate the pathogenesis of AS and provide valuable potential gene biomarkers or targets for the diagnosis and treatment of AS.

Keywords: Ankylosing spondylitis; Biomarker; Differentially expressed genes; RNA sequencing; Serum.

Fig. 1

A scatter plot and B volcano plot. The red dots represent upregulated DEGs, and the green dots represent downregulated DEGs. C Hierarchical clustering of differentially expressed genes between the AS and control groups

Fig. 2

GO, KEGG, Reactome, and GSEA analyses of the differentially expressed genes in AS patient serum. A GO analysis. BP, CC, and MF are represented in red, blue, and green, respectively. B KEGG analysis. The six KEGG pathways are shown. C Reactome analysis. The six Reactome pathways. D GSEA enriched pathway. The top ten pathways (≤ 10) are shown

Fig. 2

GO, KEGG, Reactome, and GSEA analyses of the differentially expressed genes in AS patient serum. A GO analysis. BP, CC, and MF are represented in red, blue, and green, respectively. B KEGG analysis. The six KEGG pathways are shown. C Reactome analysis. The six Reactome pathways. D GSEA enriched pathway. The top ten pathways (≤ 10) are shown

Fig. 3

Protein–protein interaction (PPI) networks and modules. A PPI network of DEGs was analyzed using Cytoscape software. The size and color of the nodes corresponding to each gene were determined according to the degree of interaction. The size of the nodes reflects the degree value, where the larger the node, the greater the degree value. The closer to the blue node, the higher connectivity between two nodes. B PPI network for the top ten hub genes. C and D Graphic representation of top two significant modules of the PPI network. (C Module 1, D Module 2)

Fig. 4

qRT-PCR-based validation of the expression of six differentially expressed genes in control and AS patient serum

Fig. 5

Visualization and details of the ROC curve