Genome‑wide association studies identified that a series of genes, including solute carrier family (SLC) 2 member 9 (SLC2A9), SLC 22 member 12 (SLC22A12) and ATP‑binding cassette sub‑family G member 2 (ABCG2) polymorphisms were associated with serum uric acid (SUA) levels in the present study. High incidence rates of hyperuricemia were reported in the Chinese population of the southeast coastal region; however, no evidence has confirmed the genetic association with SUA levels in this region. The present study aimed to investigate the association between uric acid levels and hyperuricemia, and genotypes of the Chinese population of the southeast coastal region. In the present study, a total of 1,056 healthy patients attending routine checkups were employed to investigate the incidence of hyperuricemia; 300 subjects were then randomly selected from the 1,056 patients for the identification of genetic polymorphisms of SLC2A9rs11722228, SLC22A12rs893006 and ABCG2rs2231142 via high‑resolution melting. The present study reported that the incidence rate of hyperuricemia was 32.6% (42.5% in males and 22.7% in females, respectively). The prevalence of ABCG2rs2231142 polymorphisms (CC, CA and AA) was 44.4, 44.8 and 11.8%, respectively; SLC2A9rs11722228 polymorphisms (CC, CT and TT) were reported to be 49.3, 40.3 and 10.3%, respectively. Additionally, SLC22A12rs893006 polymorphisms (CC, CT and TT) were determined to be 57.2, 38.7 and 4.1%, respectively. The SUA levels were observed to be statistically different among each investigated genotype of ABCG2rs2231142 (P=0.047). The A allele was significantly associated with an increased risk of hyperuricemia (odds ratio=2.405 and 1.133 for CA and AA, respectively). The present study reported that high incidence rates of hyperuricemia in the Chinese population of the southeast coastal region may be closely associated with the variants of ABCG2rs2231142. Whether polymorphisms of SLC2A9rs11722228 and SLC22A12rs893006 are involved in hyperuricemia require further investigation.

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Background Gout is the most prevalent inflammatory arthritis, its gold standard of diagnosis is detection of monosodium urate crystals in joints. However, the invasive test limited its use in the diagnosis of gout. Thus, there is an urgent need to exploit a novel biomarker to predict and early diagnose the gout flare. Methods In this study, we aimed to screen out the potential biomarkers of gout from GEO database (GSE178825) through bioinformatics analysis. Results The results showed that 4994 DEGs (43 up-regulated genes and 13 down-regulated genes) were identified between gout patients and healthy control. DEGs were mostly enriched in DNA repair, sphingolipid biosynthetic process, membrane. MAN1A2 was the most important hub genes in the PPI network.And then a series of enrichment bioinformatics methods were performed, cricMAN1A2 was selected as novel biomarker, which levels was measured in 30 gout patients, 30 hyperuricemia patients and 30 healthy controls by qRT-PCR. Subsequently, ROC (receiver operating characteristic cuver) were used to evaluated the potential role of cricMAN1A2 as biomarker for gout. The levels of circMAN1A2 was significantly lower in the gout patients than those in healthy controls, with higher diagnostic efficiency(AUC(area under the ROC curve) = 0.86). Conclusions Our results provide key cricRNAs related to gout, and cricMAN1A2 could be a novel serum biomarker for gout diagnosis.