Identification and bioinformatics analysis of lncRNAs in serum of patients with ankylosing spondylitis.

OBJECTIVES: The aim of this study was to explore the long non-coding RNA (lncRNA) expression profiles in serum of patients with ankylosing spondylitis (AS). The role of these lncRNAs in this complex autoimmune situation needs to be evaluated. METHODS: We used high-throughput whole-transcriptome sequencing to generate sequencing data from three patients with AS and three normal controls (NC). Then, we performed bioinformatics analyses to identify the functional and biological processes associated with differentially expressed lncRNAs (DElncRNAs). We confirmed the validity of our RNA-seq data by assessing the expression of eight lncRNAs via quantitative reverse transcription polymerase chain reaction (qRT-PCR) in 20 AS and 20 NC samples. We measured the correlation between the expression levels of lncRNAs and patient clinical index values using the Spearman correlation test. RESULTS: We identified 72 significantly upregulated and 73 significantly downregulated lncRNAs in AS patients compared to NC. qRT-PCR was performed to validate the expression of selected DElncRNAs; the results demonstrated that the expression levels of MALAT1:24, NBR2:9, lnc-DLK1-35:13, lnc-LARP1-1:1, lnc-AIPL1-1:7, and lnc-SLC12A7-1:16 were consistent with the sequencing analysis results. Enrichment analysis showed that DElncRNAs mainly participated in the immune and inflammatory responses pathways, such as regulation of protein ubiquitination, major histocompatibility complex class I-mediated antigen processing and presentation, MAPkinase activation, and interleukin-17 signaling pathways. In addition, a competing endogenous RNA network was constructed to determine the interaction among the lncRNAs, microRNAs, and mRNAs based on the confirmed lncRNAs (MALAT1:24 and NBR2:9). We further found the expression of MALAT1:24 and NBR2:9 to be positively correlated with disease severity. CONCLUSION: Taken together, our study presents a comprehensive overview of lncRNAs in the serum of AS patients, thereby contributing novel perspectives on the underlying pathogenic mechanisms of this condition. In addition, our study predicted MALAT1 has the potential to be deeply involved in the pathogenesis of AS.

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

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.