Epistasis between ADIPOQ rs1501299 and PON1 rs662 polymorphisms is potentially associated with the development of knee osteoarthritis

SNPs-Panel Polymorphism Variations in GHRL and GHSR Genes Are Not Associated with Prostate Cancer

Prostate cancer (PCa) is a major public health problem worldwide. Recent studies have suggested that ghrelin and its receptor could be involved in the susceptibility to several cancers such as PCa, leading to their use as an important predictive way for the clinical progression and prognosis of cancer. However, conflicting results of single nucleotide polymorphisms (SNPs) with ghrelin (GHRL) and its receptor (GHSR) genes were demonstrated in different studies. Thus, the present case-control study was undertaken to investigate the association of GHRL and GHSR polymorphisms with the susceptibility to sporadic PCa. A cohort of 120 PCa patients and 95 healthy subjects were enrolled in this study. Genotyping of six SNPs was performed: three tag SNPs in GHRL (rs696217, rs4684677, rs3491141) and three tag SNPs in the GHSR (rs2922126, rs572169, rs2948694) using TaqMan. The allele and genotype distribution, as well as haplotypes frequencies and linked disequilibrium (LD), were established. Multifactor dimensionality reduction (MDR) analysis was used to study gene-gene interactions between the six SNPs. Our results showed no significant association of the target polymorphisms with PCa (p > 0.05). Nevertheless, SNPs are often just markers that help identify or delimit specific genomic regions that may harbour functional variants rather than the variants causing the disease. Furthermore, we found that one GHSR rs2922126, namely the TT genotype, was significantly more frequent in PCa patients than in controls (p = 0.040). These data suggest that this genotype could be a PCa susceptibility genotype. MDR analyses revealed that the rs2922126 and rs572169 combination was the best model, with 81.08% accuracy (p = 0.0001) for predicting susceptibility to PCa. The results also showed a precision of 98.1% (p < 0.0001) and a PR-AUC of 1.00. Our findings provide new insights into the influence of GHRL and GHSR polymorphisms and significant evidence for gene-gene interactions in PCa susceptibility, and they may guide clinical decision-making to prevent overtreatment and enhance patients' quality of life.

Whole-transcriptome sequencing and ceRNA interaction network of temporomandibular joint osteoarthritis

Purpose: The aim of this study was to conduct a comprehensive transcriptomic analysis to explore the potential biological functions of noncoding RNA (ncRNAs) in temporomandibular joint osteoarthritis (TMJOA). Methods: Whole transcriptome sequencing was performed to identify differentially expressed genes (DEGs) profiles between the TMJOA and normal groups. The functions and pathways of the DEGs were analyzed using Metascape, and a competitive endogenous RNA (ceRNA) network was constructed using Cytoscape software. Results: A total of 137 DEmRNAs, 65 DEmiRNAs, 132 DElncRNAs, and 29 DEcircRNAs were identified between the TMJOA and normal groups. Functional annotation of the DEmRNAs revealed that immune response and apoptosis are closely related to TMJOA and also suggested key signaling pathways related to TMJOA, including chronic depression and PPAR signaling pathways. We identified vital mRNAs, including Klrk1, Adipoq, Cryab, and Hspa1b. Notably, Adipoq expression in cartilage was significantly upregulated in TMJOA compared with normal groups (10-fold, p < 0.001). According to the functional analysis of DEmRNAs regulated by the ceRNA network, we found that ncRNAs are involved in the regulation of autophagy and apoptosis. In addition, significantly DEncRNAs (lncRNA-COX7A1, lncRNA-CHTOP, lncRNA-UFM1, ciRNA166 and circRNA1531) were verified, and among these, circRNA1531 (14.5-fold, p < 0.001) and lncRNA-CHTOP (14.8-fold, p < 0.001) were the most significantly downregulated ncRNAs. Conclusion: This study showed the potential of lncRNAs, circRNAs, miRNAs, and mRNAs may as clinical biomarkers and provides transcriptomic insights into their functional roles in TMJOA. This study identified the transcriptomic signatures of mRNAs associated with immunity and apoptosis and the signatures of ncRNAs associated with autophagy and apoptosis and provides insight into ncRNAs in TMJOA.

Role of adiponectin in osteoarthritis

Osteoarthritis (OA) is a widespread and most common joint disease which leads to social cost increasing accompany with aging population. Surgery is often the final treatment option. The major progression of OA includes cartilage degradation caused by chondrocytes metabolism imbalance. So, the molecular mechanisms of action in chondrocytes may provide insights into treatment methods for OA. Adiponectin is an adipokine with many biological functions in the cell metabolism. Numerous studies have illustrated that adiponectin has diverse biological effects, such as inhibition of cell apoptosis. It regulates various functions in different organs, including muscle, adipose tissue, brain, and bone, and regulates skeletal homeostasis. However, the relationship between adiponectin and cell death in the progression of OA needs further investigation. We elaborate the structure and function and the effect of adiponectin and state the correlation and intersection between adiponectin, autophagy, inflammation, and OA. From the perspective of oxidative stress, apoptosis, pyroptosis, and autophagy, we discuss the possible association between adiponectin, chondrocyte metabolism, and inflammatory factor efforts in OA. What’s more, we summarize the possible treatment methods, including the use of adiponectin as a drug target, and highlight the potential future mechanistic research. In this review, we summarize the molecular pathways and mechanisms of action of adiponectin in chondrocyte inflammation and death and the pathogenesis of OA. We also review the research on adiponectin as a target for treating OA. These studies provide a novel perspective to explore more effective treatment options considering the complex interrelationship between inflammation and metabolism in OA.

Associations between adipokines gene polymorphisms and knee osteoarthritis: a meta-analysis

Background

Adipokines gene polymorphisms are speculated to be associated with the risk of knee osteoarthritis (OA), but evidence remains conflicting. This study therefore aimed to examine whether associations exist between adipokines gene polymorphisms and knee OA by considering the evidence collected from eligible studies through a meta-analysis.

Methods

A systematic search was performed on PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), and Wanfang up to March 31, 2020. Meta-analysis was carried out by focusing on the associations between adipokines gene polymorphisms and knee OA with the allele model, dominant model, and recessive model.

Results

The present meta-analysis included 5 eligible studies for ADIPOQ rs1501299 with 1,021 cases and 1,097 controls, 3 eligible studies for ADIPOQ rs2241766 with 549 cases and 544 controls, 3 eligible studies for LEPR rs1137101 with 808 cases and 856 controls, 2 eligible studies for VISFATIN rs4730153 with 339 cases and 680 controls and 2 eligible studies for VISFATIN rs16872158 with 339 cases and 680 controls. Significant association was observed between LEPR rs1137101 and knee OA in the overall population (recessive: OR = 0.40, 95% CI 0.21–0.79). Limited data revealed that associations may exist between ADIPOQ rs2241766 and knee OA in Asians (dominant: OR = 1.35, 95% CI 1.03–1.78), between VISFATIN rs4730153 and knee OA in Asians (allele: OR = 0.58, 95% CI 0.41–0.83; dominant: OR = 0.57, 95% CI 0.39–0.83), and between VISFATIN rs16872158 and knee OA in Asians (allele: OR = 1.84, 95% CI 1.26–2.68; dominant: OR = 1.94, 95% CI 1.31–2.89).

Conclusions

Adipokines gene polymorphisms may be associated with knee OA. The association was observed in LEPR rs1137101 in the present study. In addition, limited data revealed that associations may also exist in ADIPOQ rs2241766, VISFATIN rs4730153 and VISFATIN rs16872158.

Prospero registration

CRD42020187664.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-05111-4.

Identification of differential key biomarkers in the synovial tissue between rheumatoid arthritis and osteoarthritis using bioinformatics analysis

INTRODUCTION/OBJECTIVES

Rheumatoid arthritis (RA) and osteoarthritis (OA) are two common joint diseases with similar clinical manifestations. Our study aimed to identify differential gene biomarkers in the synovial tissue between RA and OA using bioinformatics analysis and validation.

METHOD

GSE36700, GSE1919, GSE12021, GSE55235, GSE55584, and GSE55457 datasets were downloaded from the Gene Expression Omnibus database. A total of 57 RA samples and 46 OA samples were included. The differentially expressed genes (DEGs) were identified. The Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were also performed. Protein-protein interaction (PPI) network of DEGs and the hub genes were constructed and visualized via Search Tool for the Retrieval of Interacting Genes/Proteins, Cytoscape, and R. Selected hub genes were validated via reverse transcription-polymerase chain reaction.

RESULTS

A total of 41 DEGs were identified. GO functional enrichment analysis showed that DEGs were enriched in immune response, signal transduction, regulation of immune response for biological process, in plasma membrane and extracellular region for cell component, and antigen binding and serine-type endopeptidase activity for molecular function. KEGG pathway analysis showed that DEGs were enriched in cytokine-cytokine receptor interaction and chemokine signaling pathway. PPI network analysis established 70 nodes and 120 edges and 15 hub genes were identified. The expression of CXCL13, CXCL10, and ADIPOQ was statistically different between RA and OA synovial tissue.

CONCLUSION

Differential expression of CXCL13, CXCL10, and ADIPOQ between RA and OA synovial tissue may provide new insights for understanding the RA development and difference between RA and OA. Key Points • Bioinformatics analysis was used to identify the differentially expressed genes in the synovial tissue between rheumatoid arthritis and osteoarthritis. • CXCL13, CXCL10, and ADIPOQ might provide new insight for understanding the differences between RA and OA.

Epistasis of polymorphisms related to the articular cartilage extracellular matrix in knee osteoarthritis: Analysis-based multifactor dimensionality reduction

Osteoarthritis (OA) is a complex disease with a multifactorial etiology. The genetic component is one of the main associated factors, resulting from interactions between genes and environmental factors. The aim of this study was to identify gene-gene interactions (epistasis) of the articular cartilage extracellular matrix (ECM) in knee OA. Ninety-two knee OA patients and 147 healthy individuals were included. Participants were genotyped in order to evaluate nine variants of eight genes associated with ECM metabolism using the OpenArray technology. Epistasis was analyzed using the multifactor dimensionality reduction (MDR) method. The MDR analysis showed significant gene-gene interactions between MMP3 (rs679620) and COL3A1 (rs1800255), and between COL3A1 (rs1800255) and VEGFA (rs699947) polymorphisms, with information gain values of 3.21% and 2.34%, respectively. Furthermore, in our study we found interactions in high-risk genotypes of the HIF1AN, MMP3 and COL3A1 genes; the most representative were [AA+CC+GA], [AA+CT+GA] and [AA+CT+GG], respectively; and low-risk genotypes [AA+CC+GG], [GG+TT+GA] and [AA+TT+GA], respectively. Knowing the interactions of these polymorphisms involved in articular cartilage ECM metabolism could provide a new tool to identify individuals at high risk of developing knee OA.

Multifactor dimensionality reduction reveals a strong gene-gene interaction between STC1 and COL11A1 genes as a possible risk factor of knee osteoarthritis

Articular cartilage is an avascular tissue with a structure that allows it to support and cushion the overload of the surfaces in contact. It maintains its metabolic functions due to the contribution of different signaling pathways. However, several factors play a role in its deterioration, allowing to the development of osteoarthritis (OA), and one of the major factors is genetic. Our goal was to identify gene-gene interactions (epistasis) between five signaling pathways involved in the articular cartilage metabolism as possible indicators of OA risk. We applied the Multifactor-Dimensionality Reduction (MDR) method to identify and characterize the epistasis between 115 SNPs located in 73 genes related to HIF-1α, Wnt/β-catenin, cartilage extracellular matrix metabolism, oxidative stress, and uric acid transporters. Ninety three patients diagnosed with primary knee OA and 150 healthy controls were included in the study. Genotyping was performed with the OpenArray system, the statistical analysis was carried out with the STATA software v14, and epistasis was analyzed with the MDR software v3.0.2. The MDR analysis revealed that the best interaction model was between polymorphisms rs17786744 of the STC1 gene and rs2615977 of the COL11A1 gene, with an entropy value of 4.44%, CVC 8/10, OR 5.60, 95% CI 3.27-9.59, p < 0.0001. Under this interaction model, we identified high and low risk genotypes involved in OA development. Our results suggest complex interactions between STC1 and COL11A1 genes that might have an impact on genetic susceptibility to develop OA. Further studies are required to confirm it.

Impact of the gene-gene interactions related to the HIF-1α signaling pathway with the knee osteoarthritis development

INTRODUCTION/OBJECTIVES

Articular cartilage is the target tissue of osteoarthritis (OA), and because it lacks capillary networks, the microenvironment is hypoxic. Hypoxia inducible factor-1 alpha (HIF-1α) regulates the homeostasis of this tissue. The aim of this study was to investigate whether genetic polymorphisms of the HIF-1α signaling pathway are involved in the development of knee OA.

METHOD

We performed a case-control association study and genotyped 134 knee OA patients and 267 healthy controls. All participants were genotyped in order to evaluate 42 SNPs from 22 genes involved in the HIF-1α signaling pathway using the OpenArray technology. Gene-gene interactions (epistasis) were analyzed using the multifactor dimensionality reduction (MDR) method.

RESULTS

The MDR analysis showed epistasis between AKT2 (rs8100018) and IGF1 (rs2288377), AKT2 (rs8100018) and IGF1 (rs35767), IGF1 (rs35767) and COL2A1 (rs1793953), and between GSK3B (rs6438552) and IGF1 (rs35767) polymorphisms, with information gain values of 21.24%, 8.37%, 9.93%, and 5.73%, respectively. Additionally, our model allowed us to identify high- and low-risk genotypes among COL2A1 rs1793953, GSK3B rs6438552, AKT2 rs8100018, and IGF1 rs35767 polymorphisms.

CONCLUSIONS

Knowing the interactions of these polymorphisms involved in HIF-1α signaling pathway could provide a new diagnostic support tool to identify individuals at high risk of developing knee OA.

Network Analyses of Differentially Expressed Genes in Osteoarthritis to Identify Hub Genes

Background

Osteoarthritis (OA) is the most common degenerative disease in orthopedics. However, the cause and underlying molecular mechanisms are not clear. This study aims to identify the hub genes and pathways involved in the occurrence of osteoarthritis.

Methods

The raw data of GSE89408 were downloaded from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) were identified by R software. The DAVID database was used for pathway and gene ontology analysis, and p<0.05 and gene count >2 were set as the cut-off point. Moreover, protein-protein interaction (PPI) network construction was applied for exploring the hub genes in osteoarthritis. The expression levels of the top ten hub genes in knee osteoarthritis synovial membranes and controls were detected by quantitative real-time PCR system.

Results

A total of 229 DEGs were identified in osteoarthritis synovial membranes compared with normal synovial membranes, including 145 upregulated and 84 downregulated differentially expressed genes. The KEGG pathway analysis results showed that up-DEGs were enriched in proteoglycans in cytokine-cytokine receptor interaction, chemokine signaling pathway, rheumatoid arthritis, and TNF signaling pathway, whereas down-DEGs were enriched in the PPAR signaling pathway and AMPK signaling pathway. The qRT-PCR results showed that the expression levels of ADIPOQ, IL6, and CXCR1 in the synovium of osteoarthritis were significantly increased (p <0.05).