Identification of key genes underlying the effects of obesity on knee osteoarthritis.
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ABSTRACT:
SUBMITTER: Dai S
PROVIDER: S-EPMC8869650 | biostudies-literature | 2021 Sep
REPOSITORIES: biostudies-literature
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Identification of key genes underlying the effects of obesity on knee osteoarthritis.
Dai Siming S Zhang Juan J Zhu Xiaoying X Lin Yuxuan Y Cui Ying Y Zhang Zhiyi Z Lin Zhiguo Z
Chinese medical journal 20210921 4
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Project description:Knee osteoarthritis (KOA) is a common chronic joint disease globally. Synovial inflammation plays a pivotal role in its pathogenesis, preceding cartilage damage. Identifying biomarkers in osteoarthritic synovial tissues holds promise for early diagnosis and targeted interventions. Gene expression profiles were obtained from the Gene Expression Omnibus database. Subsequent analyses included differential expression gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) on the combined datasets. We performed functional enrichment analysis on the overlapping genes between DEGs and module genes and constructed a protein-protein interaction network. Using Cytoscape software, we identified hub genes related to the disease and conducted gene set enrichment analysis on these hub genes. The CIBERSORT algorithm was employed to evaluate the correlation between hub genes and the abundance of immune cells within tissues. Finally, Mendelian randomization analysis was utilized to assess the potential of these hub genes as biomarkers. We identified 46 differentially expressed genes (DEGs), comprising 20 upregulated and 26 downregulated genes. Using WGCNA, we constructed a gene co-expression network and selected the most relevant modules, resulting in 24 intersecting genes with the DEGs. KEGG enrichment analysis of the intersecting genes identified the IL-17 signaling pathway, associated with inflammation, as the most significant pathway. Cytoscape software was utilized to rank the candidate genes, with JUN, ATF3, FOSB, NR4A2, and IL6 emerging as the top five based on the Degree algorithm. A nomogram model incorporating these five genes, supported by ROC curve analysis, validated their diagnostic efficacy. Immune infiltration and correlation analysis revealed that macrophages were significantly associated with JUN (p < 0.01), FOSB (p < 0.01), and NR4A2 (p < 0.05). Additionally, T follicular helper cells showed significant associations with ATF3 (p < 0.05), FOSB (p < 0.05), and JUN (p < 0.05). Mendelian randomization analysis provided strong evidence linking JUN (IVW: OR = 0.910, p = 0.005) and IL6 (IVW: OR = 1.024, p = 0.026) with KOA. Through the utilization of various bioinformatics analysis methods, we have pinpointed key hub genes relevant to knee osteoarthritis. These findings hold promise for advancing pre-symptomatic diagnostic strategies and enhancing our understanding of the biological underpinnings behind knee osteoarthritis susceptibility genes.
| S-EPMC11439059 | biostudies-literature
Project description:BackgroundAt present, the progression mechanism of knee osteoarthritis (KOA) has not been fully elucidated, and there is a clinical need for late KOA-specific diagnostic markers to provide reference for preventive treatment. This study aimed to analyze the sequencing results of early- and late-stage KOA synovial tissue based on the key genes of late-stage KOA in combination with a machine learning algorithm.MethodsThe whole transcriptome sequencing results of synovial tissue from KOA patients (GSE176223 and GSE32317) were downloaded from the gene expression omnibus (GEO) database. Thirty-nine early KOA synovial tissue samples and 31 late KOA synovial tissue samples were included in this study. The diagnostic criteria and baseline data balance of early and late KOA were referred to the data source literature, and the two groups of data had good baseline data balance. R software (V3.5.1) and R packages were used for screening and enrichment analysis of differentially expressed genes (DEGs). The key genes were screened by weighted correlation network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression analysis. A receiver operating characteristic curve (ROC) curve was used to evaluate the diagnostic efficacy of key genes for advanced KOA.ResultsA total of 211 DEGs related to knee arthritis were screened out. Compared with synovial tissue of early knee arthritis, 111 genes were upregulated and 100 genes were downregulated in the synovial tissue of late knee arthritis. Sixty-six key genes were screened out through WGCNA and 34 key genes were screened out in the LASSO analysis. The genes obtained by the two algorithms combined with three overlapping genes, namely interleukin- 6 (IL-6), C-X-C chemokine ligand 12 (CXCL12), and macrophage migration inhibitor factor (MIF). The areas under the ROC curves of CXCL12, IL-6, and MIF were 0.96, 0.944, and 0.961, respectively (P<0.001).ConclusionsIL-6, CXCL12, and MIF are the key pathogenic genes of KOA, which have good diagnostic efficacy for advanced KOA.
| S-EPMC9577766 | biostudies-literature
Project description:BackgroundIncreasing evidence has suggested that obesity affects the occurrence and progression of osteoarthritis (OA). However, the underlying molecular mechanism that obesity affects the course of OA is not fully understood and remains to be studied.MethodsThe gene expression profiles of the GSE117999 and GSE98460 datasets were derived from the Gene Expression Omnibus (GEO) database. Firstly, we explored the correlation between obesity and OA using chi-square test. Next, weighted gene coexpression network analysis (WGCNA) was executed to identify obesity patients with OA- (obesity OA-) related genes in the GSE117999 dataset by "WGCNA" package. Moreover, differential expression analysis was performed to select the hub genes by "limma" package. Furthermore, ingenuity pathway analysis (IPA) and functional enrichment analysis ("clusterProfiler" package) were conducted to investigate the functions of genes. Finally, the regulatory networks of hub genes and protein-protein interaction (PPI) network were created by the Cytoscape 3.5.1 software and STRING.ResultsA total of 15 differentially expressed obesity OA-related genes, including 9 lncRNAs and 6 protein coding genes, were detected by overlapping 66 differentially expressed genes (DEGs) between normal BMI samples and obesity OA samples and 451 obesity OA-related genes. Moreover, CCR10, LENG8, QRFPR, UHRF1BP1, and HLA-DRB4 were identified as hub genes. IPA results indicated that the hub genes were noticeably enriched in antimicrobial response, inflammatory response, and humoral immune response. PPI network showed that CCR10 interacted more with other proteins. Gene set enrichment analysis (GSEA) indicated that the hub genes were related to protein translation, cancer, chromatin modification, antigen processing, and presentation.ConclusionOur results further demonstrated the role of obesity in OA and might provide new targets for the treatment of obesity OA.
| S-EPMC9293492 | biostudies-literature
Project description:Epigenetic changes in articular chondrocytes are associated with osteoarthritis (OA) disease progression. Numerous studies have identified differentially methylated cytosines in OA tissues; however, the consequences of altered CpG methylation at single nucleotides on gene expression and phenotypes are difficult to predict. With the objective of detecting novel genes relevant to OA, we conducted a genome-wide assessment of differentially methylated sites (DMSs) and differentially methylated regions (DMRs). DNA was extracted from visually damaged and normal appearing, non-damaged human knee articular cartilage from the same joint and then subjected to reduced representation bisulfite sequencing. DMRs were identified using a genome-wide systematic bioinformatics approach. A sliding-window of 500 bp was used for screening the genome for regions with clusters of DMSs. Gene expression levels were assessed and cell culture demethylation experiments were performed to further examine top candidate genes associated with damaged articular cartilage. More than 1000 DMRs were detected in damaged osteoarthritic cartilage. Nineteen of these contained five or more DMSs and were located in gene promoters or first introns and exons. Gene expression assessment revealed that hypermethylated DMRs in damaged samples were more consistently associated with gene repression than hypomethylated DMRs were with gene activation. Accordingly, a demethylation agent induced expression of most hypermethylated genes in chondrocytes. Our study revealed the utility of a systematic DMR search as an alternative to focusing on single nucleotide data. In particular, this approach uncovered promising candidates for functional studies such as the hypermethylated protein-coding genes FOXP4 and SHROOM1, which appear to be linked to OA pathology in humans and warrant further investigation.
| S-EPMC4679536 | biostudies-literature
Project description:Objective: To examine the changes in tibial plateau cartilage in relation to body mass index (BMI) in patients with end-stage osteoarthritis (OA). Design: Knees were obtained from 23 OA patients (3 non-obese, 20 obese) at the time of total knee replacement. RNA prepared from cartilage was probed for differentially expressed (DE) gene transcripts using RNA microarrays and validated via real-time PCR. Differences with regard to age, sex, and between medial and lateral compartments were also queried. Results: Microarrays revealed that numerous transcripts were significantly DE between non-obese and obese patients (≥1.5-fold) using pooled and separate data from medial and lateral compartments. Correlation analyses showed that 706 transcripts (459 positively, 247 negatively) were significantly correlated with BMI. Among these, HS3ST6, HSD17B12, and FAM26F were positively correlated while STAC3, PRSS21, and EDA were negatively correlated. Differentially correlated transcripts represented important biological processes e.g. cellular metabolic processes, anatomical structure morphogenesis and cellular response to growth factors. Although age and sex had some effect on transcript expression, most intriguing results were observed for comparison between medial and lateral compartments. Transcripts (MMP13, CLEC3A, MATN3, EPYC, SCARNA5, COL2A1) elevated in the medial compartment represented skeletal system development, cartilage development, collagen and proteoglycan metabolism, and extracellular matrix organization. Likewise, transcripts (SELE, CTSS, VSIG4, F13A1, and STEAP4) repressed in medial compartment represented host immune response, cell migration, wound healing, cell proliferation and response to cytokines. PCR data confirmed expression of DE transcripts. Conclusions: This study supports molecular interaction between obesity and OA and implies that BMI is an important determinant of transcript-level changes in cartilage.
2017-05-03 | GSE98460 | GEO
Project description: Not available
| S-EPMC10496305 | biostudies-literature
Project description:Background: Osteoarthritis (OA) is one of the main causes of disability in the elderly population, accompanied by a series of underlying pathologic changes, such as cartilage degradation, synovitis, subchondral bone sclerosis, and meniscus injury. The present study aimed to identify key genes, signaling pathways, and miRNAs in knee OA associated with the entire joint components, and to explain the potential mechanisms using computational analysis. Methods: The differentially expressed genes (DEGs) in cartilage, synovium, subchondral bone, and meniscus were identified using the Gene Expression Omnibus 2R (GEO2R) analysis based on dataset from GSE43923, GSE12021, GSE98918, and GSE51588, respectively and visualized in Volcano Plot. Venn diagram analyses were performed to identify the overlapping DEGs (overlapping DEGs) that expressed in at least two types of tissues mentioned above. Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, protein-protein interaction (PPI) analysis, and module analysis were conducted. Furthermore, qRT-PCR was performed to validate above results using our clinical specimens. Results: As a result, a total of 236 overlapping DEGs were identified, of which 160 were upregulated and 76 were downregulated. Through enrichment analysis and constructing the PPI network and miRNA-mRNA network, knee OA-related key genes, such as HEY1, AHR, VEGFA, MYC, and CXCL12 were identified. Clinical validation by qRT-PCR experiments further supported above computational results. In addition, knee OA-related key miRNAs such as miR-101, miR-181a, miR-29, miR-9, and miR-221, and pathways such as Wnt signaling, HIF-1 signaling, PI3K-Akt signaling, and axon guidance pathways were also identified. Among above identified knee OA-related key genes, pathways and miRNAs, genes such as AHR, HEY1, MYC, GAP43, and PTN, pathways like axon guidance, and miRNAs such as miR-17, miR-21, miR-155, miR-185, and miR-1 are lack of research and worthy for future investigation. Conclusion: The present informatic study for the first time provides insight to the potential therapeutic targets of knee OA by comprehensively analyzing the overlapping genes differentially expressed in multiple joint components and their relevant signaling pathways and interactive miRNAs.
| S-EPMC8419250 | biostudies-literature
Project description:The present study aimed to identify potential key genes and pathways in obese children in order to explore possible molecular mechanisms associated with child obesity. The array dataset GSE29718 was downloaded from the Gene Expression Omnibus database. Subcutaneous adipose tissue samples derived from 7 obese children and 8 lean children were selected for the analysis. Differentially expressed genes (DEGs) in samples from obese children compared with those from lean children were analyzed by the limma package. Gene ontology (GO) annotation, Kyoto Encyclopedia of Genes and Genomes and Reactome pathway enrichment analyses for up and downregulated genes were performed. A protein-protein interaction (PPI) network was constructed with Cytoscape software and important genes associated with obesity were determined using IRegulon. A total of 199 DEGs (79 up and 120 downregulated genes) were identified in the samples of obese children compared with those from lean children. The PPI network was established with 103 nodes and 147 protein pairs. Matrix metalloproteinase 9 (MMP9) and acetyl-CoA carboxylase β (ACACB) were identified as hub genes in the PPI network and may therefore be marker genes for child obesity. In addition, upregulated DEGs were enriched in Reactome pathways associated with the immune system. Besides, MMP9 was upregulated in immune system processes as a GO term in the category Biological Processes. The results of the present study indicated that MMP9, ACACB and immune system pathways may have a significant role in child obesity.
| S-EPMC5525596 | biostudies-literature
Project description:Rheumatoid arthritis (RA) and osteoarthritis (OA) comprise the most common forms of arthritis. The aim of this study was to identify differentially expressed genes (DEGs) and associated biological processes between RA and OA using a bioinformatics approach to elucidate their potential pathogenesis.The gene expression profiles of the GSE55457 datasets, originally produced through use of the high-throughput Affymetrix Human Genome U133A Array, were downloaded from the Gene Expression Omnibus (GEO) database. The GSE55457 dataset contains information from 33 samples, including 10 normal control (NC) samples, 13 RA samples, and 10 OA samples. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed to identify functional categories and associated molecular and biochemical pathways, respectively, for the identified DEGs, and a protein-protein interaction (PPI) network of the DEGs was constructed using Cytoscape software.GO and KEGG results suggested that several biological pathways (ie, "immune response," "inflammation," and "osteoclast differentiation") are commonly involved in the development of both RA and OA, whereas several other pathways (eg, "MAPK signaling pathway," and "ECM-receptor interaction") presented significant differences between these disorders.This study provides further insights into the underlying pathogenesis of RA and OA, which may facilitate the diagnosis and treatment of these diseases.
| S-EPMC6392928 | biostudies-literature
Project description:BackgroundOsteoarthritis (OA) represents a prominent etiology of considerable pain and disability, and conventional imaging methods lack sensitivity in diagnosing certain types of OA. Therefore, there is a need to identify highly sensitive and efficient biomarkers for OA diagnosis. Zinc ions feature in the pathogenesis of OA. This work aimed to investugate the role of zinc metabolism-related genes (ZMRGs) in OA and the diagnostic characteristics of key genes.MethodsWe obtained datasets GSE169077 and GSE55235 from the Gene Expression Omnibus (GEO) and obtained ZMRGs from MSigDB. Differential expression analysis was conducted on the GSE169077 dataset using the limma R package to identify differentially expressed genes (DEGs), and the intersection of DEGs and ZMRGs yielded zinc metabolism differential expression-related genes (ZMRGs-DEGs). The clusterProfiler R package was employed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of ZMRGs-DEGs. Potential small molecule drugs were predicted using the CMap database, and immune cell infiltration and function in OA individuals were analyzed using the ssGSEA method. Protein-protein interaction (PPI) networks were constructed to detect Hub genes among ZMRGs-DEGs. Hub gene expression levels were analyzed in the GSE169077 and GSE55235 datasets, and their diagnostic characteristics were assessed using receiver operating characteristic (ROC) curves. The gene-miRNA interaction network of Hub genes was explored using the gene-miRNA interaction network website.ResultsWe identified 842 DEGs in the GSE169077 dataset, and their intersection with ZMRGs resulted in 46 ZMRGs-DEGs. ZMRGs-DEGs were primarily enriched in functions such as collagen catabolic processes, extracellular matrix organization, metallopeptidase activity, and pathways like the IL-17 signaling pathway, Nitrogen metabolism, and Relaxin signaling pathway. Ten potential small-molecule drugs were predicted using the CMap database. OA patients exhibited distinct immune cell abundance and function compared to healthy individuals. We identified 4 Hub genes (MMP2, MMP3, MMP9, MMP13) through the PPI network, which were highly expressed in OA and demonstrated good diagnostic performance. Furthermore, two closely related miRNAs for each of the 4 Hub genes were identified.Conclusion4 Hub genes were identified as potential diagnostic biomarkers and therapeutic targets for OA.
| S-EPMC10956297 | biostudies-literature