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Co-regulated ceRNA network mediated by circRNA and lncRNA in patients with gouty arthritis

BMC Medical Genomics volume 17, Article number: 264 (2024) Cite this article

Abstract

Numerous studies have demonstrated the involvement of messenger RNAs (mRNAs) and non-coding RNAs, including long non-coding RNAs (lncRNA), circular RNAs (circRNAs) and microRNA (miRNAs), in gouty arthritis onset; however, the regulatory mechanism has not yet been elucidated. Here, we applied whole-transcriptome sequencing to identify the differentially expressed circRNAs, lncRNAs, miRNAs and mRNAs between the gout patients and normal people, and constructed co-regulated networks of circRNAs and lncRNAs according to the competitive endogenous RNA (ceRNA) theory for gouty arthritis onset to improve our understanding of the pathogenesis of this disease. The most significant finding of this study is the co-regulated ceRNA network of circRNAs and lncRNAs in gouty arthritis. The circRNA novel_circ_0030384 and the lncRNAs AAMP, TRIM16, PKN1, XLOC_184579 and XLOC_189826 were upstream genes in the co-regulated network. These upstream genes upregulated miR550a-5p and miR550a-3-5p, which downregulated PSME1 and FERMT3 expression. These mRNAs participated in proteasome dynamics, antigen processing and presentation, and platelet activation, which are associated with inflammation in gouty arthritis. In addition, the circRNA and lncRNAs upregulated miR550a-5p, which downregulated GRK2 and OS9 expression. Also, it proved that the down-regulated of PSME1, FERMT3, GRK2 and OS9 can aggravate gouty arthritis in vitro. In summary, these genes mediate inflammation in gouty arthritis through chemokine signaling to regulate neutrophil function.

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Introduction

Gouty arthritis is an acute inflammatory reaction caused by the accumulation of monosodium uric acid in the joints [1]. A recent study has linked the pathogenesis of gouty arthritis to the Toll-like receptor (TLR) pathway, NF-KB signaling pathway and NLRP3 signaling pathway [2]. However, the pathogenesis of gouty arthritis has not been elucidated, although several studies have reported that genetic factors play key roles in regulating gene expression during disease onset [3, 4]. In terms of gene expression in humans, protein-coding genes account for less than 2% of the genome, while non-coding RNAs (ncRNAs) account for more than 90% of the genome [5]. Therefore, it is important to identify ncRNAs in order to map the regulatory network of gouty arthritis.

Most ncRNAs, including miRNAs, lncRNAs and circRNAs, are regulatory RNAs [6]. In brief, miRNAs inhibit the translation of mRNAs and regulate the expression of genes at the post-transcriptional level, while lncRNAs and cirRNAs interact with miRNAs, mRNAs and even proteins. Among these mechanisms, lncRNAs and cirRNAs function as competitive endogenous RNAs (ceRNAs) by communicating with mRNAs and competing for shared miRNAs [7]. However, there are few studies on the relationships between ceRNAs and gouty arthritis, and there is no information on the co-regulated ceRNA network of circRNAs and lncRNAs.

In this study, we compared the differential expression profiles of circRNAs, lncRNAs, miRNAs and mRNAs of gouty arthritis patients with healthy individuals. In addition, we investigated the GO enriched functions and KEGG enriched pathways of the differentially expressed RNAs and constructed a co-regulated ceRNA network of circRNAs and lncRNAs. This study provides novel insights into the regulatory mechanism of gouty arthritis.

Materials and methods

Ethical statement

This study was conducted with the approval of the ethics committee of Chongqing Hospital of Traditional Chinese Medicine (approval number: 2020-KY-YJS-XYQ). All participants signed informed consent forms. All experiments were conducted in strict accordance with the Helsinki Declaration.

Study participants

For the case group, ten Chinese patients were recruited from the outpatient rheumatology clinic of Chongqing Hospital of Traditional Chinese Medicine. All patients were diagnosed with gouty arthritis according to the criteria of the American College of Rheumatology [8]. For the control group, ten healthy individuals without a history of gouty arthritis, hyperuricemia or metabolic disease were selected. The mean age of cases and controls were 40.0 ± 13.1 years and 39.6 ± 12.4 years, respectively. All participants had no history of major organ or hematological disease(s). Patients who met any of the following conditions were excluded: (i) severe organ dysfunction, (ii) taking non steroidal anti-inflammatory drug or glucocorticoids, (iii) joint deformities caused by gouty arthritis. Whole peripheral blood samples was collected during the acute onset of gouty arthritis. Whole peripheral blood samples were collected during the acute onset of gouty arthritis. Blood was collected using 2 ml EDTA anticoagulant tubes. Within 30 min after collection, samples were stored in a -80 °C freezer for later use".

RNA isolation and quantification

RNA was extracted from whole peripheral blood samples using the TRIzol method. Briefly, cell lysis was performed using TRIzol reagent, followed by addition of chloroform and centrifugation at 12,000 × g for 15 min at 4 °C to separate the phases. The aqueous phase containing RNA was precipitated with isopropanol. The RNA pellet was washed with 75% ethanol, air-dried, and resuspended in RNase-free water. RNA quantity and quality were assessed using a NanoDrop spectrophotometer and Agilent 2100 Bioanalyzer (Fig. 1), and RNA degradation and contamination were monitored on 1% agarose gels. RNA purity and concentration were assessed using the NanoPhotometer® spectrophotometer (IMPLEN, CA, USA), and RNA integrity and quantity were measured using the RNA Nano 6000 assay kit of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA).

Fig. 1
figure 1

Flowchart of data processing and analysis

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High-throughput sequencing and quantification of gene expression levels

All sequencing (seq) experiments were performed by Novogene (Beijng, China). The details of circRNA-seq, lncRNA-seq, miRNA-seq and mRNA-seq, as well as the steps of library preparation, are described in the Supplementary Materials and Methods. The expression levels of miRNAs were estimated by transcript per million (TPM) using previously published criteria [9]. The expression levels of lncRNAs and mRNAs were assessed by StringTie-1.3.3b software, and the reads per kilobase of transcript per million mapped reads (RPKM) were obtained. The circRNA raw counts were normalized using TPM as follows: (read count × 1,000,000) / libsize. The libsize is the sum of the circRNA read count.

Identification of differentially expressed miRNAs, circRNAs, lncRNAs and mRNAs

For the circRNA and miRNA samples with biological replicates, the DESeq2_1.24.0 tool within R package was used to perform differential expression analysis of the two groups. The resulting p-values were adjusted using the Benjamini–Hochberg approach for controlling the false discovery rate. Genes with an adjusted p-value, as determined by the DESeq tool, were considered differentially expressed. For the circRNA and miRNA samples without biological replicates, the DEGse tool (2010) within R package was used to perform differential expression analysis of the two groups. The resulting p-values were adjusted using q-values. Q-value < 0.01 and |log2(foldchange)|> 1 were set as the thresholds for significantly differential expression.

For the lncRNA and mRNA samples, Cuffdiff was used for differential expression analysis. The resulting p-values were adjusted using the Benjamini–Hochberg approach for controlling the false discovery rate. Genes with |log2 (foldchange)|> 0 and padj < 0.05 were considered differentially expressed.

Target gene prediction and construction of lncRNA–miRNA, cirRNA–miRNA and miRNA–mRNA networks, as well as the ceRNA (circRNA–miRNA–mRNA, lncRNA–miRNA–mRNA, co-regulated circRNA and lncRNA) regulatory network.

Target genes of the differentially expressed miRNAs, miRNA-lncRNA and miRNA-circRNA interactions were predicted using the DIANALncBase v2 package. Target genes and miRNAs showed contrasting patterns in their differential expression. Cytoscape (http://www.cytoscape.org/) was employed to visualize the lncRNA–miRNA, cirRNA–miRNA and miRNA–mRNA regulatory networks. Next, the miRNAs were screened, and the mRNAs with a targeted relationship and a negative expression correlation with the miRNA, as well as the circRNAs and lncRNAs with a targeted relationship and a negative expression correlation with the miRNA, were input into Cytoscape for construction of circRNA–miRNA–mRNA and lncRNA–miRNA–mRNA networks, as well as the co-regulated ceRNA network of lncRNAs and circRNAs.

Enriched term and pathway analyses

The analysis of Gene Ontology (GO) enriched functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) enriched pathways of the differentially expressed genes related to gouty arthritis was performed. GO analysis examines genes in terms of their molecular functions biological processes and cellular components. For both GO functions and KEGG pathways, p-values less than 0.05 indicated a statistically significant difference. GSEA analysis was performed using R, primarily employing the clusterProfiler and enrichplot packages. Initially, differential expression data and the h.all.v2023.2.Hs.symbols.gmt gene set were imported. Enrichment analysis was then conducted using the GSEA function from clusterProfiler. Results were visualized using functions such as gseaplot2 from the enrichplot package, generating enrichment plots and heatmaps. Finally, significantly enriched gene sets were interpreted based on FDR values and Normalized Enrichment Scores (NES), revealing potential biological pathways and functions.

RNA extraction and quantitative real-time PCR (qRT-PCR)

Briefly, RNA from cells was extracted using TRIzol Reagent (Invitrogen, Life Technologies, Inc., Germany) according to the manufacturer’s instructions. Next, complementary DNA was synthesized using the Prime Script RT Master Mix Kit (Takara, Tokyo, Japan) and served as a template for real-time PCR using the FastFire qPCR PreMix reagent (Tiangen Biotech, Beijing, China) according to the manufacturer’s instructions. GAPDH was used as the internal control gene, and relative gene expression was analyzed using the 2 − ΔΔCt method.

Western blot assay

The human monocytic cell line THP-1 was selected as it can be differentiated into macrophage-like cells relevant for studying gouty arthritis. THP-1 cells were cultured in RPMI 1640 medium supplemented with 10% FBS at 37 °C with 5% CO2. Cells were transfected with miR-550a-5p mimics or negative control using Lipofectamine 3000 reagent according to the manufacturer's protocol. Protein extracts containing protease inhibitors (Bimake, #B14001) were prepared using RIPA buffer (Beyotime, China). Protein concentrations were measured using the BCA kit (Biosharp, #BL521A). Equal amounts of total protein were separated by SDS–polyacrylamide gel electrophoresis and transferred onto a PVDF membrane (Merck Millipore, ISEQ00010). The membrane was then blocked for 1 h with 5% non-fat dry milk in 0.1% TBST. The blots were incubated with primary antibodies on a shaker at 4 ◦C overnight and then incubated with secondary antibodies for 1 h at room temperature. After 3 × 5 min washes in TBST, chemiluminescent signals were detected using ECL detection reagent (Amersham Biosciences, UK).

Results

Differential expression profiles of lncRNAs, circRNAs, miRNAs and mRNAs

A total of 273 lncRNAs, 1672 circRNAs, 333 miRNAs and 2148 mRNAs were differentially expressed between case and control groups. Among them, 175 lncRNAs, 1216 circRNAs, 147 miRNAs and 983 mRNAs were upregulated, while 98 lncRNAs, 456 circRNAs, 186 miRNAs and 1165 mRNAs were downregulated (Fig. 2A–D). A heatmap was generated for cluster analysis of differentially expressed lncRNAs (Fig. 2E). Hierarchical clustering was performed to generate a heatmap of differentially expressed circRNAs (Fig. 2F). The differentially expressed miRNAs were visualized using volcano plots and hierarchical clustering (Fig. 2G). The differentially expressed transcripts are shown in a heatmap (Fig. 2H).

Fig. 2
figure 2

Expression profiles of differential expression RNAs. A, E: Expression profiles of lncRNAs. B, F: Expression profiles of circRNAs. C, G: Expression profiles of miRNAs. D, H. Expression profiles of mRNAs. A, B, C, D: In the volcano plots, green points represent downregulated RNAs, while red points represent upregulated RNAs. E, F, G, H: In the heatmap, the rectangles represent decreased RNA expression, while red rectangles represent increased RNA expression

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Construction of miRNA–lncRNA, miRNA–circRNA and miRNA–mRNA networks

We screened out 226 differentially expressed lncRNAs from our database that were related to 34 differentially expressed miRNAs. The miRNA–lncRNA regulatory network was comprised of 831 miRNA–lncRNA pairs (Table 1). There were 1666 differentially expressed circRNAs in our database that were related to 334 differentially expressed miRNAs. The miRNA–circRNA regulatory network was comprised of 23,720 miRNA–circRNA pairs (Table 1). We identified 1711 differentially expressed mRNAs in our database that were related to 320 differentially expressed miRNAs. The miRNA–mRNA regulatory network was comprised of 12,344 miRNA–mRNA pairs (Table 1).

Table 1 Construction of miRNA-lncRNA, miRNA-circRNA and miRNA-mRNA network
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Analysis of the ceRNA network, GO enriched functions and KEGG enriched pathways of the circRNA–miRNA–mRNA network

The circRNA–miRNA–mRNA network was generated based on the relationships among the differentially expressed circRNAs, miRNAs and mRNAs (Fig. 3A). This network was comprised of 8 circRNA nodes, 6 miRNA nodes, 31 mRNA nodes and 54 edges. The pathways of the circRNA–miRNA–mRNA network were mainly enriched in glycosaminoglycan biosynthesis–chondroitin sulfate/dermatan sulfate, glycosaminoglycan biosynthesis–heparan sulfate/heparin, endocytosis, proteasome dynamics and RIG-I-like receptor signaling, as determined by KEGG pathway analysis (Fig. 3B). The most significantly enriched biological processes of the circRNA–miRNA–mRNA network included biological processes, cellular processes, single-organism processes and metabolic processes (Fig. 3C).

Fig. 3
figure 3

A: circRNA-miRNA-mRNA ceRNA network. B: KEGG enrichment analysis of circRNA-miRNA-mRNA. C: GO enrichment analysis of circRNA-miRNA-mRNA

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Analysis of the ceRNA network, GO enriched functions and KEGG pathways of the lncRNA–miRNA–mRNA network

Based on the relationships among the differentially expressed lncRNAs, miRNAs and mRNAs, the lncRNA–miRNA–mRNA network was generated. This network was comprised of 6 lncRNA nodes, 3 miRNA nodes, 5 mRNA nodes and 18 edges (Fig. 4A). The pathways of the lncRNA–miRNA–mRNA network were mainly enriched in galactose metabolism, endocytosis, spliceosome dynamics, 2-oxocarboxylic acid metabolism, glycosaminoglycan biosynthesis–chondroitin sulfate/dermatan sulfate, glycosaminoglycan biosynthesis–heparan sulfate/heparin, pentose phosphate pathway and RIG-I-like receptor signaling, as determined by KEGG pathway analysis (Fig. 4B). The most significantly enriched biological processes of the lncRNA–miRNA–mRNA network included biological processes, cellular processes, single-organism processes and metabolic processes (Fig. 4C).

Fig. 4
figure 4

A: lncRNA-miRNA-mRNA ceRNA network. B: KEGG enrichment analysis of lncRNA-miRNA-mRNA. C: GO enrichment analysis of lncRNA-miRNA-mRNA

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Analysis of the co-regulated ceRNA network, GO enriched functions and KEGG enriched pathways of the circRNA–lncRNA–miRNA–mRNA network

Based on the results of circRNA–miRNA–mRNA and lncRNA–miRNA–mRNA networks, a co-regulated ceRNA network of circRNAs and lncRNAs was generated (Fig. 5A). This network was comprised of 1 circRNA node, 5 lncRNA nodes, 2 miRNA nodes, 4 mRNA nodes and 18 edges. Using this approach, we identified novel coding and non-coding RNAs that may be involved in gouty arthritis, including novel_circ_0030384, AAMP, TRIM16, PKN1, XLOC_184579, XLOC_189826, miR-550a-5p, miR-550a-3-5p, GRK2, PSME1, FERMT3 and OS9 (Table 2). The pathways of the co-regulated ceRNA network of lncRNAs and circRNAs were mainly enriched in proteasome dynamics, antigen processing and presentation, platelet activation, morphine addiction, glutamatergic synapse function, endocytosis, chemokine signaling and protein processing in the endoplasmic reticulum, as determined by KEGG pathway analysis (Fig. 5A).The most significantly enriched biological processes of the co-regulated ceRNA network of lncRNAs and circRNAs included single-organism metabolic processes and regulation of biological quality (Fig. 5B). In addition, we performed GSEA enrichment analysis on the RNA-seq results from disease and normal patient groups. The results showed that pathways such as HALLMARK_HEME_METABOLISM, HALLMARK_UV_RESPONSE_DN, HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION, HALLMARK_ANGIOGENESIS, and HALLMARK_COAGULATION were activated, while pathways like HALLMARK_WNT_BETA_CATENIN_SIGNALING were suppressed (Supplemental FigureS1A-F).

Fig. 5
figure 5

A: Co-regulated ceRNA network of circRNA and lncRNA. B: GO enrichment analysis of the co-regulated ceRNA network

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Table 2 The details of circRNA- and lncRNA-miRNA-mRNA ceRNA related to gout
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Down-regulated of PSME1, FERMT3, GRK2 and OS9 aggravated gouty arthritis in vitro

To further validate the effect of miR-550a-5p in the expression of PSME1, FERMT3, GRK2 and OS9, we transfected miR-550a-5p in human macrophage cell line THP-1 for further validation based on its central position in the ceRNA network and high number of predicted target genes. As shown in Fig. 6A, the expression of PSME1, FERMT3, GRK2 and OS9 was decreased after miR-550a-5p overexpression. Then, THP-1 cells were stimulated with 100ug/mL Monosodium Urate crystals (MSU) for 3 h to simulate in vitro gouty arthritis. As predicted by RNA-seq, the expression of PSME1, FERMT3, GRK2 and OS9 was also decreased after MSU treatment (Fig. 6B). We further performed loss-of-function experiments to determine the effect of PSME1, FERMT3, GRK2 and OS9 on inflammatory response of gouty arthritis. As shown in Fig. 6C and 6D, silencing PSME1, FERMT3, GRK2 and OS9 significantly increased TNF-α and IL-1β levels in the supernatant of THP-1 cells. Consisted with the results of ELISA, the result of western blot also demonstrated silencing PSME1, FERMT3, GRK2 and OS9 increased the protein levels of p-p65 and cleaved caspase-1, indicating PSME1, FERMT3, GRK2 and OS9 might inhibited gouty arthritis via NF-kb/NLRP3 signaling pathway.

Fig. 6
figure 6

Down-regulated of PSME1, FERMT3, GRK2 and OS9 aggravated gouty arthritis in vitro. (A) The expression of PSME1, FERMT3, GRK2 and OS9 was detected by qRT-PCR after transfected with miR-550a-5p in THP-1 cells. (B) The expression of PSME1, FERMT3, GRK2 and OS9 was detected by qRT-PCR in THP-1 cells after treatment with MSU (100ug/mL) for 3 h. (C-D) The TNF-α and IL-1β levels in the supernatant of THP-1 cells were detected by ELISA after MSU treatment and transfected with siPSME1, siFERMT3, siGRK2 and siOS9. (E) The protein levels of p-p65 and cleaved caspase-1 was measured by western blot after the knockdown of PSME1, FERMT3, GRK2 and OS9

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Discussion

Gouty arthritis is an inflammatory disease caused by the deposition of monosodium urate crystals in joints due to the disruption of purine metabolism. Several studies have reported that monosodium urate can accelerate proteasome activity in macrophages, activating the NLRP3 pathway, TLR pathway and NF-KB pathway to release IL-1β, which plays a major role in the initiation of the inflammatory response in gouty arthritis [10,11,12]. In addition, the serum level of uric acid is heritable, and mutations in genes that encode enzymes in the purine salvage pathway have been recognized as a cause of gouty arthritis [13]. However, the pathogenesis of gouty arthritis is still not well understood. Other studies have demonstrated that lncRNAs, circRNAs and miRNAs participate in the inflammatory response of a gouty arthritis attack and regulate related signaling pathways [14, 15]. In terms of the regulatory relationships, lncRNAs and circRNAs can compete with miRNAs through miRNA response elements to form a ceRNA network and play a regulatory role in gouty arthritis [16]. However, there are few studies of gouty arthritis and the lncRNA-associated ceRNA network [17, 18]. Thus far, there is no relevant report on the circRNA-associated ceRNA network or the co-regulated ceRNA network of lncRNAs and circRNAs in the pathogenesis of gouty arthritis. Our study focuses on the differentially expressed RNAs in patients with gouty arthritis and generates a co-regulated ceRNA network of circRNAs and lncRNAs.

In this study, we constructed a co-regulated ceRNA network of circRNAs and lncRNAs after building circRNA–miRNA–mRNA and lncRNA–miRNA–mRNA ceRNA networks. These co-regulated ceRNA networks are comprised of upstream lncRNAs (AAMP, TRIM16, PKN1, XLOC_184579, XLOC_189826), a circRNA (novel_circ0030384) and miRNAs (miR550a-5p and miR550a-3-5p), as well as downstream mRNAs (PSME1, FERMTS, GRK2, OS9).

In the co-regulated ceRNA network, the five lncRNAs (AAMP, TRIM16, PKN1, XLOC_184579, XLOC_189826) affected mRNAs (PSME1, FERMTS, GRK2, OS9), which were downregulated by miR550a-5p. The same lncRNAs downregulated the mRNAs through miR550a-3-5p. In a previous study, AAMP participated in NOD2-mediated NF-KB activation [19]. TRIM16 facilitates autophagic degradation of protein aggregates, streamlines the process of stress-induced aggregate clearance and protects cells against oxidative/proteotoxic stress-induced toxicity [20]. PKN1 plays a role in cilia dynamics and cancer biology, as well as the PI3K-AKT signaling pathway [21]. XLOC_184579, XLOC_189826 and novel_circ0030384 are new genes, with novel_circ0030384 reversing the inhibition of miR-550a-3-5p and miR-550a-5p. Novel_circ0030384 come from gene TDRD3 which related to the HIF1-α pathway [22]. The downstream genes of these five lncRNAs and one circRNA were GRK2, PSME1, FERMTS and OS9. GRK2 has been reported to be expressed in human neutrophils and GRK2 inhibitors can promote neutrophil production [23]. Neutrophils participate in gouty arthritis by inducing neutrophil extracellular trap formation and releasing cytokines and chemokines that promote inflammation [24]. Thus, a decreased level of GRK2 regulates neutrophil function through chemokine signaling, thereby resulting in gouty arthritis, which is consistent with the KEGG pathway analysis of AAMP, TRIM16, PKN1, XLOC_184579, XLOC_189826, miR-550a-5p and GRK2. PSME1, FERMTS and OS9, which are not related to gouty arthritis, may be new target genes for gouty arthritis onset. PSME1 can inhibit the expression of β-catenin, thereby inactivating WNT/β-catenin signaling [25]. In addition, NF-KB and NLRP3 inflammasome signaling can mediate inflammation by promoting the secretion of WNT ligands through WNT signaling [26]. PSME1 mediates proteasome signaling, suggesting that PSME1 is associated with inflammation. FERMT3 is a marker of anti-inflammatory type II macrophages [27]. Based on the KEGG pathway analysis, FERMT3 is associated with platelet activation, which is required for the inflammatory response [28]. OS9 is mediates endoplasmic reticulum-to-Golgi transport of dendritic cell-specific transmembrane protein in response to TLR activation, suggesting a novel role for OS9 in myeloid differentiation and cell fusion [29]. OS9 also mediates protein processing in the endoplasmic reticulum. These three anti-inflammatory-related mRNAs (GRK2, PSME1, FERMTS) downregulate mRNAs, which lead to inflammation through proteasome activation, platelet activation and chemokine signaling, and thus, gouty arthritis.

The expression of lncRNAs (AAMP, TRIM16, PKN1, XLOC_ 184,579, XLOC_ 189,826) decreased, which reversed the inhibition of downstream miRNAs. The reduced expression of novel_ circ_ 0030384 decreased binding to downstream miRNAs and increased the inhibitory effects of miRNAs (miR-550a-3-5p and miR-550a-5p) on downstream mRNAs. In general, miRNAs silence the expression of downstream mRNAs, so increases in miRNA levels can lead to decreases in mRNA levels. The elevated expression of miR-550a-3-5p decreased the expression of PSME1 and FERMT3. At the same time, the elevated expression of miR-550a-5p inhibited the expression of PSME1, FERMT3, GRK2 and OS9. Thus, the reduced expression of PSME1, FERMT3, GRK2 and OS9 may cause gouty arthritis. The most significant finding of this study is that novel_circ_0030384, as well as AAMP, TRIM16, PKN1, XLOC_184579 and XLOC_189826, are upstream genes in this co-regulated network. These genes upregulate miR550a-5p, which downregulates GRK2 expression to regulate neutrophil function. Importantly, we used the preclinical methods to prove the function of these genes in vitro. These findings suggest that inflammation in gouty arthritis is activated by neutrophils, and both circRNAs and lncRNAs participate in the activation of the neutrophils through miR550a-5p.

We elucidated the co-regulated profiles of circRNAs and lncRNAs in patients with gouty arthritis. These findings expand our knowledge on ceRNA biology and contribute to our understanding of their regulation in the pathogenesis of gouty arthritis. In addition, these novel networks are comprised of potential biomarkers and therapeutic targets for gouty arthritis. However, we did not verify the gene interactions in the regulatory network and did not investigate the influence of drugs on the regulatory network. Further studies are needed to examine the correlations between drug efficacy and gene expression, as well as to explore the pathogenesis, treatment response and recurrence of gouty arthritis.

Also, this study has several limitations that should be acknowledged. The sample size of 10 patients and 10 controls is relatively small, which may limit the generalizability of our findings. Further studies with larger cohorts are needed to validate these results. Additionally, while we performed in vitro functional experiments for key genes, more extensive functional validation of the identified circRNAs and lncRNAs is warranted. Future studies should include gain-of-function and loss-of-function experiments to fully elucidate their roles in gouty arthritis pathogenesis.

Conclusion

In this study, we identified a novel co-regulated network of circRNAs and lncRNAs in gouty arthritis, based on the competitive endogenous RNA (ceRNA) theory. Our analysis revealed that novel_circ_0030384, AAMP, TRIM16, PKN1, XLOC_184579, and XLOC_189826 act as upstream regulators in this network. These co-regulated genes were found to upregulate miR550a-5p, which in turn downregulates GRK2 expression.

Our findings suggest the existence of a complex ceRNA network involving circRNAs, lncRNAs, miRNAs, and mRNAs in gouty arthritis. This network may play a role in regulating inflammation through chemokine signaling pathways. However, it is important to note that these results should be interpreted with caution due to the limitations of our study.The small sample size of 10 patients and 10 controls may limit the generalizability of our findings. Additionally, while we performed some in vitro functional experiments, more extensive validation of the identified circRNAs and lncRNAs is warranted. Future studies with larger cohorts and comprehensive functional assays, including gain-of-function and loss-of-function experiments, are necessary to fully elucidate the roles of these RNA molecules in gouty arthritis pathogenesis.

Despite these limitations, our study provides valuable insights into the potential regulatory mechanisms underlying gouty arthritis. The identified RNA molecules may serve as promising candidates for further investigation as potential therapeutic targets or biomarkers. However, additional research is needed to validate these hypotheses and explore their clinical implications.In conclusion, our study offers a foundation for understanding the complex RNA regulatory networks in gouty arthritis. While our findings are promising, they should be considered as hypotheses requiring further validation. Future research in this direction may contribute to improved diagnosis, treatment, and management of gouty arthritis.

Data Availability

No datasets were generated or analysed during the current study.

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Acknowledgements

The study was supported by the Chongqing Science and Technology Commission (grant no.cstc2019jscx-dxwtBX0023). We thank International Science Editing ( http://www.internationalscienceediting.com ) for editing this manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Author notes

  1. Yanqiu Xu, Jiayu Tian, Miao Wang, Jinkun Liu, Wenfu Cao and Bin wu contributed equally to the manuscript.

Authors and Affiliations

  1. College of Traditional Chinese Medicine, Chongqing Medical University, Chongqing, 400016, People’s Republic of China

    Yanqiu Xu, Wenfu Cao & Bin wu

  2. Department of Rheumatology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, 400021, People’s Republic of China

    Yanqiu Xu, Jiayu Tian, Miao Wang, Jinkun Liu & Bin wu

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  1. Yanqiu Xu
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Contributions

Yanqiu Xu (YX): First author, responsible for research concept and design, data collection, data analysis and interpretation, drafting the original manuscript, and reviewing and editing the manuscript. Jiayu Tian (JT): Data analysis and interpretation, reviewing and editing the manuscript. Miao Wang (MW): Data collection, data analysis and interpretation, reviewing and editing the manuscript. Jinkun Liu (JL): Data analysis and interpretation, literature review, reviewing and editing the manuscript. Wenfu Cao (WC): Data analysis and interpretation, experimental operations, reviewing and editing the manuscript. Bin Wu (BW): Experimental design, experimental operations, data analysis and interpretation, reviewing and editing the manuscript.

Corresponding authors

Correspondence to Wenfu Cao or Bin wu.

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Ethics approval and consent to participate

This study was approved by the ethics committee of Chongqing Hospital of Traditional Chinese Medicine (approval number: 2020-KY-YJS-XYQ).Informed consent was obtained from all patients and/or their parents or legal guardians.

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Not Applicable.

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The authors declare no competing interests.

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Supplementary Information

12920_2024_2038_MOESM1_ESM.docx

Additional file 1: Supplemental Figure S1A-E. GSEA enrichment analysis results showing pathways with activated activity. The curve plots demonstrate the enrichment of genes in the ranked list. F. GSEA enrichment analysis results showing pathways with suppressed activity. The curve plot demonstrates the enrichment of genes in the ranked list.

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Xu, Y., Tian, J., Wang, M. et al. Co-regulated ceRNA network mediated by circRNA and lncRNA in patients with gouty arthritis. BMC Med Genomics 17, 264 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12920-024-02038-8

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Keywords

BMC Medical Genomics

ISSN: 1755-8794

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