METHODS: The
circRNA microarray technology was used to detect the expression of
circRNAs in the peripheral blood of 6 patients with AS and 6 healthy controls (HC). To screen the differentially expressed
circRNAs by fold change (FC) and P value, these differentially expressed
circRNAs were analyzed by bioinformatics. In 60 cases of AS and 30 cases of HC, 4
circRNAs were subjected to real-time fluorescence quantitative polymerase chain reaction (RT-qPCR), and their correlation with various clinical indicators was analyzed. Finally, the receiver operating characteristic (ROC) curve was used to analyze their potential as AS diagnostic markers.
RESULTS: The microarray results showed that there were 1369 significantly differently expressed (P < 0.05, FC > 1.5)
circRNAs between the AS and HC groups (675 upregulated and 694 downregulated). The results of bioinformatics analysis suggested that they were mainly involved in "
enzyme binding," "
adenosine ribonucleotide binding," "MAPK signaling pathway", etc. The RT-qPCR results showed that the expressions of hsa_
circRNA_001544 (U = 486.5, P < 0.05) and hsa_
circRNA_102532 (U = 645, P < 0.05) were significantly different between the AS group and the HC group. The AS group was further divided into two subgroups: active AS (ASA) and stable AS (ASS). After analysis, it was found that compared with the HC group, hsa_
circRNA_001544 was significantly increased in both ASA (U = 214, P < 0.05) and ASS groups (U = 273, P < 0.05), while hsa_
circRNA_008961 (U = 250, P < 0.05) and hsa_
circRNA_102532 (U = 295, P < 0.05) were only significantly increased in the ASA group. Furthermore, hsa_
circRNA_012732 was significantly different between the ASA and ASS groups (U = 194, P < 0.05), and there was no statistical significance among the remaining groups. Correlation analysis results showed that hsa_
circRNA_012732 was negatively correlated with Bath
Ankylosing Spondylitis Disease Activity Index (BASDAI),
high-sensitivity C-reactive protein (
hsCRP), and
globulin (GLOB) and positively correlated with lymphocyte count (LY), mean corpusular volume, and
albumin (ALB), and hsa_
circRNA_008961 was negatively correlated with platelet (PLT) count. ROC curve analysis showed that hsa_
circRNA_001544 (95% CI = 0.610-0.831, P < 0.05) and hsa_
circRNA_102532 (95% CI = 0.521-0.762, P < 0.05) were statistically significant, and their area under curve (AUC) values were 0.720 and 0.642, respectively.
CONCLUSIONS: There are differentially expressed
circRNAs in PBMCs of AS patients, and they may be involved in the occurrence and development of AS. Among these differentially expressed
circRNAs, hsa_
circRNA_012732 has the potential to become an
indicator of disease activity, and hsa_
circRNA_001544 has the potential to become a molecular marker for AS diagnosis.