ALBERT

Description: To determine the expression profile and clinical significance of long non-coding RNAs (lncRNAs) in peripheral blood mononuclear cells (PBMCs) of patients with primary gout and healthy control subjects. Human lncRNA microarrays were used to identify the differentially expressed lncRNAs and mRNAs in primary gout patients (n = 6) and healthy control subjects (n = 6). Bioinformatics analyses were performed to predict the roles of differently expressed lncRNAs and mRNAs. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect the expression levels of 8 lnRNAs in 64 primary gout patients and 32 healthy control subjects. Spearman’s correlation was used to analyze the correlation between these eight lncRNAs and the laboratory values of gout patients. A receiver operating characteristic (ROC) curve was constructed to evaluate the diagnostic value of the lncRNAs identified in gout. The microarray analysis identified 1479 differentially expressed lncRNAs (879 more highly expressed and 600 more lowly expressed), 862 differentially expressed mRNAs (390 more highly expressed and 472 more lowly expressed) in primary gout (fold change 〉 2, P 〈 0.05), respectively. The bioinformatic analysis indicated that the differentially expressed lncRNAs regulated the abnormally expressed mRNAs, which were involved in the pathogenesis of gout through several different pathways. The expression levels of TCONS_00004393 and ENST00000566457 were significantly increased in the acute gout flare group than those in the intercritical gout group or healthy subjects (P

Description: Circular RNAs (circRNAs) are non-coding RNAs (ncRNAs) with a single-stranded covalently closed-loop structure, and their abnormal expression may participate in the pathogenesis of various human diseases. Currently, knowledge of circRNAs in gout is limited. In this case-control study, human circRNA microarrays were used to identify differentially expressed circRNAs in peripheral blood mononuclear cells (PBMCs) from patients with primary gout (n = 5) and healthy controls (HC; n = 3). Bioinformatics methods were used to analyze significantly different circRNAs (fold change 〉1.5, p 〈 0.05). In addition, four significantly differentially expressed circRNAs were selected for quantitative real-time polymerase chain reaction to detect expression levels in 90 gout patients and 60 HC. Subsequently, circRNA-miRNA-mRNA network was established to predict the function of circRNAs of interest. Microarray analysis indicated that 238 circRNAs were upregulated and 41 circRNAs were down-regulated in the gout group (fold change 〉1.5, p 〈 0.05). Bioinformatics analysis showed that differentially expressed circRNAs were involved in the pathogenesis of gout via various pathways. Moreover, the expression levels of hsa_circRNA_103657 and hsa_circRNA_000241 were significantly higher in the gout group than those in the HC group, and both correlated significantly with lipid metabolism parameters. Furthermore, the area under the curve of hsa_circRNA_103657 was 0.801 (95% confidence interval (CI): 0.730–0.871; p 〈 0.001). Our results provide novel insights into the pathogenesis of primary gout. Differentially expressed circRNAs were identified in the PBMCs of gout patients, and these differential circRNAs may play important roles in the development and progression of gout.