LncRNAs Landscape in the patients of primary gout by microarray analysis

Development and Validation of Prognostic Characteristics Associated With Chromatin Remodeling-Related Genes in Ovarian Cancer

BACKGROUND

Ovarian cancer (OC) is a prevalent malignant tumor in the field of gynecology, exhibiting the third highest incidence rate and the highest mortality rate among gynecological tumors. Chromatin remodeling accomplishes specific chromatin condensation at distinct genomic loci and plays an essential role in epigenetic regulation associated with various processes related to cancer development.

METHODS

Differentially expressed genes (DEGs) between OC and control samples were screened from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, combined with chromatin remodeling-related genes (CRRGs) obtained from the GeneCards database to identify differentially expressed CRRGs (DECRRGs). Enrichment analysis and protein-protein interaction (PPI) network were performed on the DECRRGs. Prognostic genes of OC were screened using univariate Cox and least absolute shrinkage and selection operator (Lasso) analyses. A risk model based on prognostic genes was developed, and the survival probability of OC patients in different risk groups was analyzed by Kaplan-Meier (KM) curve. Finally, the expression levels of prognostic genes were validated by quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting.

RESULTS

In total, 7 potential prognostic genes associated with the progression of OC patients were obtained, including ARID1B, ATRX, CHRAC1, HDAC1, INO80, MBD2, and SS18. Based on the expression level of prognostic genes, OC patients were divided into high-risk group and low-risk group. Survival analysis indicated that patients classified into the high-risk group had higher mortality rates, which enables this prediction model to be utilized as an independent predictor of OC. Immunocorrelation analysis showed that low-risk patients were more likely to benefit from immunotherapy.

CONCLUSION

In this study, we have identified 7 prognostic genes, including ARID1B, ATRX, CHRAC1, HDAC1, INO80, MBD2, and SS18. Overall, our findings provided a foundation for further comprehension of the potential molecular mechanisms underlying OC pathogenesis and progression.

Integrated Network Pharmacology and In-silico Approaches to Decipher the Pharmacological Mechanism of Dioscorea septemloba Thunb in Treating Gout and Its Complications

BACKGROUND

Dioscorea septemloba Thunb. (DST) has demonstrated therapeutic potential in the treatment of gout and its associated complications. However, the underlying mechanisms of DST's pharmacological activity remain unclear. This study aims to investigate the pharmacological substances and network regulatory mechanisms of DST in treating gout and its complications using network pharmacology.

METHODS

According to ultra-high performance liquid chromatography coupled with hybrid quadrupole-Orbitrap mass spectrometry (UPLC-Q-Exactive Orbitrap-MS) data and Lipinski's rule of five, 24 bioactive phytochemicals from DST were identified. The targets of gout were retrieved from Gene Expression Omnibus (GEO), GeneCards, and DisGeNET databases, followed by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG pathway) enrichment analysis. The Cytoscape network analysis was used to identify the primary pathological pathways and key targets. Finally, LeDock was used for molecular docking to verify the active components of DST and their core target proteins.

RESULTS

DST contains several core active ingredients, such as tetrahydroimidazo[1,2-a]pyridine- 2,5-dione, diosgenin, beta-sitosterol, dioscorol B, montroumarin and 9,10-dihydro-5,7- dimethoxy-3,4-phenanthrenediol. Moreover, these active components were found to strongly bind to the key targets for treating gout and its complications, including HSP90AA1, STAT3, PTGS2, PPARG, MTOR, HIF1A, MMP9, ESR1, and TLR4. As a result, DST alleviates gout and its complications by inhibiting xanthine dehydrogenase (XDH) to reduce uric acid levels and regulating the HIF-1α, EZH2/STAT3, and COX-2/PPAR-γ pathways to reduce inflammation. Additionally, it also plays an analgesic role by regulating the neuroactive ligand-receptor interaction pathway and calcium ion signaling pathway.

CONCLUSION

This study has provided insights into the underlying mechanisms of DST in the treatment of gout and its complications, which could serve as a scientific foundation for its clinical translation.

Genetic and epigenetic dysregulation of innate immune mechanisms in autoinflammatory diseases

Dysregulation and hyperactivation of innate immune responses can lead to the onset of systemic autoinflammatory diseases. Monogenic autoinflammatory diseases are caused by inborn genetic errors and based on molecular mechanisms at play, can be divided into inflammasomopathies, interferonopathies, relopathies, protein misfolding, and endogenous antagonist deficiencies. On the other hand, more common autoinflammatory diseases are multifactorial, with both genetic and non-genetic factors playing an important role. During the last decade, long-term memory characteristics of innate immune responses have been described (also called trained immunity) that in physiological conditions provide enhanced host protection from pathogenic re-infection. However, if dysregulated, induction of trained immunity can become maladaptive, perpetuating chronic inflammatory activation. Here, we describe the mechanisms of genetic and epigenetic dysregulation of the innate immune system and maladaptive trained immunity that leads to the onset and perpetuation of the most common and recently described systemic autoinflammatory diseases.

The pathogenesis of gout: molecular insights from genetic, epigenomic and transcriptomic studies

The pathogenesis of gout involves a series of steps beginning with hyperuricaemia, followed by the deposition of monosodium urate crystal in articular structures and culminating in an innate immune response, mediated by the NLRP3 inflammasome, to the deposited crystals. Large genome-wide association studies (GWAS) of serum urate levels initially identified the genetic variants with the strongest effects, mapping mainly to genes that encode urate transporters in the kidney and gut. Other GWAS highlighted the importance of uncommon genetic variants. More recently, genetic and epigenetic genome-wide studies have revealed new pathways in the inflammatory process of gout, including genetic associations with epigenomic modifiers. Epigenome-wide association studies are also implicating epigenomic remodelling in gout, which perhaps regulates the responsiveness of the innate immune system to monosodium urate crystals. Notably, genes implicated in gout GWAS do not include those encoding components of the NLRP3 inflammasome itself, but instead include genes encoding molecules involved in its regulation. Knowledge of the molecular mechanisms underlying gout has advanced through the translation of genetic associations into specific molecular mechanisms. Notable examples include ABCG2, HNF4A, PDZK1, MAF and IL37. Current genetic studies are dominated by participants of European ancestry; however, studies focusing on other population groups are discovering informative population-specific variants associated with gout.

Identification of hub genes and transcription factors in patients with primary gout complicated with atherosclerosis

Evidence shows that primary gout is prone to develop to atherosclerosis, but the mechanism of its occurrence is still not fully clarified. The aim of this study was to explore the molecular mechanism of the occurrence of this complication in gout. The gene expression profiles of primary gout and atherosclerosis were downloaded from the gene expression omnibus database. Overlapping differentially expressed genes (DEGs) between gout and atherosclerosis were identified. The biological roles of common DEGs were explored through enrichment analyses. Hub genes were identified using protein-protein interaction networks. The immune infiltrations of 28 types of immune cells in gout and control samples from GSE160170 were evaluated by the ssGSEA method. Transcription factors (TFs) were predicted using Transcriptional Regulatory Relationships Unraveled by Sentence Based Text Mining (TRRUST) database. A total of 168 overlapping DEGs were identified. Functional enrichment analyses indicated that DEGs were mostly enriched in chemokine signaling pathway, regulation of actin cytoskeleton, and TNF signaling pathway. CytoScape demonstrated 11 hub genes and two gene cluster modules. The immune infiltration analysis showed that the expression of DEGs in gout was significantly upregulated in activated CD4 T cells, gamma delta T cells, T follicular helper cell, CD56dim natural killer cells, and eosinophil. TRRUST predicted one TF, RUNX family transcription factor 1. Our study explored the pathogenesis of gout with atherosclerosis and discovered the immune infiltration of gout. These results may guide future experimental research and clinical transformation.

Transcriptomic Analysis of THP-1 Cells Exposed by Monosodium Urate Reveals Key Genes Involved in Gout

BACKGROUND

Gout is a common inflammatory arthritis, which is mainly caused by the deposition of monosodium urate (MSU) in tissues. Transcriptomics was used to explore the pathogenesis and treatment of gout in our work.

OBJECTIVE

The objective of the study was to analyze and validate potential therapeutic targets and biomarkers in THP-1 cells that were exposed to MSU.

METHODS

THP-1 cells were exposed to MSU. The inflammatory effect was characterized, and RNA-Seq analysis was then carried out. The differential genes obtained by RNA-Seq were analyzed with gene expression omnibus (GEO) series 160170 (GSE160170) gout-related clinical samples in the GEO database and gout-related genes in the GeneCards database. From the three analysis approaches, the genes with significant differences were verified by the differential genes' transcription levels. The interaction relationship of long non-coding RNA (lncRNA) was proposed by ceRNA network analysis.

RESULTS

MSU significantly promoted the release of IL-1β and IL-18 in THP-1 cells, which aggravated their inflammatory effect. Through RNA-Seq, 698 differential genes were obtained, including 606 differential mRNA and 92 differential `LncRNA. Cross-analysis of the RNA-Seq differential genes, the GSE160170 differential genes, and the gout-related genes in GeneCards revealed a total of 17 genes coexisting in the tripartite data. Furthermore, seven differential genes-C-X-C motif chemokine ligand 8 (CXCL8), C-X-C motif chemokine ligand 2 (CXCL2), tumor necrosis factor (TNF), C-C motif chemokine ligand 3 (CCL3), suppressor of cytokine signaling 3 (SOCS3), oncostatin M (OSM), and MIR22 host gene (MIR22HG)-were verified as key genes that analyzed the weight of genes in pathways, the enrichment of inflammationrelated pathways, and protein-protein interaction (PPI) nodes combined with the expression of genes in RNA-Seq and GSE160170. It is suggested that MIR22HG may regulate OSM and SOCS3 through microRNA 4271 (miR-4271), OSM, and SOCS3m; CCL3 through microRNA 149-3p (miR-149-3p); and CXCL2 through microRNA 4652-3p (miR-4652-3p).

CONCLUSION

The potential of CXCL8, CXCL2, TNF, CCL3, SOCS3, and OSM as gout biomarkers and MIR22HG as a therapeutic target for gout are proposed, which provide new insights into the mechanisms of gout biomarkers and therapeutic methods.

Expression Profile of Pyroptosis-Related Genes and the Associated Regulatory Axis in Primary Gout Patients

Purpose

Analyzed the expression characteristics of pyroptosis-related genes (PRGs) in peripheral blood mononuclear cells (PBMCs) of gout patients by microarray, and constructed ceRNA network to explore the molecular mechanism of RNA-mediated pyroptosis regulation.

Patients and Methods

Human mRNA, lncRNA, circRNA microarray data were used to identify differentially expressed in PBMCs from patients with primary gout and healthy controls. Differential PRGs in PBMCs of gout patients identified by Genecard database and mRNA microarray data. GO and KEGG enrichment analyses of these genes were then conducted. Protein-protein interaction networks and cytoHubba were used to identify hub genes. Combining the lncRNA and circRNA microarray data, a ceRNA network was constructed by Cytoscape to screen out key non-coding RNA molecules that can regulate target PRGs. Finally, the relative expression levels of target miRNA and circRNA in 60 gout patients and 40 healthy subjects were detected by qRT-PCR.

Results

The results revealed 30 differentially expressed PRGs. GO and KEGG analysis of these genes were mainly concentrated in the production and regulation of cytokines, NOD-like receptor signaling pathway and so on. Nine hub genes were screened by PPI network, including IL1B, DDX3X, NLRP3, NLRP9, AIM2, CASP8, P2XR7, CARD8 and IFI16. The has_circRNA_102906\hsa_circRNA_102910\hsa_circRNA_102911-hsa-miR-129-5p-DDX3X\NLRP3\NLRP9 regulatory network was constructed. The expression of has_circRNA_102906, hsa_circRNA_102910, hsa_circRNA_102911 were up-regulated and hsa-miR-129-5p down-regulated in PBMCs of gout patients. The relative expression of hsa_circRNA_102911 was positively correlated with clinical inflammatory indicators associated with gout, and the area under the curve of hsa_circRNA_102911 for gout diagnosis was 0.85 (95% CI: 0.775-0.925; p < 0.001).

Conclusion

There are several differentially expressed PRGs in PBMCs of gout patients, which are involved in the regulation of gout inflammation through multiple pathways. hsa_circRNA_102911-hsa-miR-129-5p-DDX3X\NLRP3\NLRP9 may be the key regulatory pathway for pyroptosis to regulate gout inflammation, and hsa_circRNA_102911 may be a potential biomarker for the diagnosis of primary gout.

Hypomethylation of the opioid receptor delta 1 gene combined with high opioid receptor delta 1 protein levels indicates increased risk of gout

Background

The purpose of this study was to identify biomarkers for the diagnosis of gout in Chinese Han males using methylation microarray profiling.

Methods

We screened for differentially methylated genes (DMGs) in gout using a methylation microarray and analyzed the functions of the DMGs using gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. We verified gene methylation levels by pyrosequencing and protein levels by enzyme‐linked immunosorbent assays (ELISAs). Statistical analyses were performed using SPSS. Two‐sided p values <0.05 were deemed to be statistically significant for all analyses.

Results

We identified 20,426 significant differential methylation sites (5719 high‐methylation sites and 14,707 low‐methylation sites). Bioinformatics analysis showed that the DMGs were mainly involved in 43 biological functions, 13 cellular components, 18 molecular functions, and 35 KEGG pathways. We selected opioid receptor delta 1 (OPRD1) for verification of methylation levels between 50 gout patients and 50 controls. The methylation levels of OPRD1 (Chr1:29,139,121) were significantly lower in the gout group (p < 0.05), while OPRD1 protein levels were significantly higher in the gout group (p < 0.05). In addition, the AUC of the combination of OPRD1 (Chr1:29,139,121) methylation and OPRD1 protein levels was 0.796 (0.710, 0.883) with a high sensitivity of 82% and a specificity of 68% (p < 0.001).

Conclusions

The combination of OPRD1 (Chr1:29,139,121) hypomethylation and high levels of OPRD1 protein is a potential biomarker for gout diagnosis.

Construction of lncRNA-miRNA-mRNA network based on ceRNA mechanism reveals the function of lncRNA in the pathogenesis of gout

Objective

To identify differentially expressed lncRNA, miRNA, and mRNA during the pathogenesis of gout, explore the ceRNA network regulatory mechanism of gout, and seek potential therapeutic targets.

Method

First, gout‐related chips were retrieved by GEO database. Then, the analysis of differentially expressed lncRNAs and mRNAs was conducted by R language and other software. Besides, miRNA and its regulated mRNA were predicted based on public databases, the intersection of differentially expressed mRNA and predicated mRNA was taken, and the lncRNA‐miRNA‐mRNA regulatory relationships were obtained to construct the ceRNA regulatory network. Subsequently, hub genes were screened by the STRING database and Cytoscape software. Then the DAVID database was used to illustrate the gene functions and related pathways of hub genes and to mine key ceRNA networks.

Results

Three hundred and eighty‐eight lncRNAs and 758 mRNAs were identified with significant differential expression in gout patient, which regulates hub genes in the ceRNA network, such as JUN, FOS, PTGS2, NR4A2, and TNFAIP3. In the ceRNA network, lncRNA competes with mRNA for miRNA, thus affecting the IL‐17 signaling pathway, TNF signaling pathway, Oxytocin signaling pathway, and NF‐κB signaling pathway through regulating the cell's response to chemical stress. The research indicates that five miRNAs (miR‐429, miR‐137, miR‐139‐5p, miR‐217, miR‐23b‐3p) and five lncRNAs (SNHG1, FAM182A, SPAG5‐AS1, HNF1A‐AS1, UCA1) play an important role in the formation and development of gout.

Conclusion

The interaction in the ceRNA network can affect the formation and development of gout by regulating the body's inflammatory response as well as proliferation, differentiation, and apoptosis of chondrocytes and osteoclasts. The identification of potential therapeutic targets and signaling pathways through ceRNA network can provide a reference for further research on the pathogenesis of gout.

H19 is involved in the regulation of inflammatory responses in acute gouty arthritis by targeting miR-2-3p

A great number of studies have confirmed that long noncoding RNA (lncRNA) are involved in the regulation of inflammatory response in acute gouty arthritis (AGA). This paper aimed to survey the regulatory mechanism of H19 on AGA. The expression of serum H19 in all subjects was examined by qRT-PCR. The ROC curve was used to estimate the diagnostic value of H19 for AGA. THP-1 cells were induced by MSU to establish in vitro AGA cell model. The concentrations of cytokines such as IL-1β, IL-8, and TNF-α were tested by ELISA. Luciferase reporter gene analysis was used to verify the interaction between H19 and the 3'-UTR of miR-22-3p. Expressions of serum H19 in AGA patients were significantly higher than that in controls. The ROC curve indicated the potential of H19 as a diagnostic marker for AGA. Cell experiments revealed that the downregulation of H19 significantly inhibited the expressions of IL-1β, IL-8, and TNF-α. The luciferase reporter gene assay manifested that miR-22-3p is the target gene of H19. And knockdown of miR-22-3p overturned the downregulation of inflammatory factors caused by H19 inhibition. H19 aggravated MSU-induced THP-1 inflammation by negatively targeting miR-22-3p, suggesting a new regulatory mechanism and potential therapeutic target for AGA.

Long noncoding RNA SNHG8 accelerates acute gouty arthritis development by upregulating AP3D1 in mice

Gout can affect the quality of life of patients due to monosodium urate monohydrate (MSU) crystals. Numerous studies have proposed that long noncoding RNAs (lncRNAs) regulate gout. We aimed to reveal the function of lncRNA small nucleolar RNA host gene 8 (SNHG8) in acute gouty arthritis (GA). A GA mouse model was established by injection of MSU into footpads. The levels of SNHG8, miR-542-3p and adaptor-related protein complex 3 subunit delta 1 (AP3D1) in footpads were detected via polymerase chain reaction analysis. Hematoxylin–eosin staining revealed the paw swelling in mice. Enzyme-linked immunosorbent assay and western blot analysis were applied to determine the concentrations of proinflammatory cytokines. SNHG8 expression was identified to be upregulated after MSU treatment. Ablation of SNHG8 decreased the MSU-induced enhancement of paw swelling and foot thickness. In addition, SNHG8 depletion decreased the protein levels of proinflammatory factors in GA mice. Mechanically, SNHG8 was verified to be a sponge of miR-542-3p, and miR-542-3p targeted AP3D1 3ʹ untranslated region. SNHG8 competitively bound with miR-542-3p to upregulate AP3D1 expression. Finally, results of rescue assays illustrated that AP3D1 upregulation offset the SNHG8-mediated inhibition on paw swelling and protein levels of proinflammatory factors in GA mice. In conclusion, SNHG8 accelerates acute GA development by upregulating AP3D1 in an miR-542-3p-dependent way in mice, providing an effective therapeutic approach to treat acute GA.