Identification of key genes in rheumatoid arthritis and osteoarthritis based on bioinformatics analysis.
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ABSTRACT:
SUBMITTER: Zhu N
PROVIDER: S-EPMC6392928 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundRheumatoid arthritis (RA) is a chronic auto-inflammatory disorder of joints. The present study aimed to identify the key genes in RA for better understanding the underlying mechanisms of RA.MethodsThe integrated analysis of expression profiling was conducted to identify differentially expressed genes (DEGs) in RA. Moreover, functional annotation, protein-protein interaction (PPI) network and transcription factor (TF) regulatory network construction were applied for exploring the potential biological roles of DEGs in RA. In addition, the expression level of identified candidate DEGs was preliminarily detected in peripheral blood cells of RA patients in the GSE17755 dataset. Quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to validate the expression levels of identified DEGs in RA.ResultsA total of 378 DEGs, including 202 up- and 176 down-regulated genes, were identified in synovial tissues of RA patients compared with healthy controls. DEGs were significantly enriched in axon guidance, RNA transport and MAPK signaling pathway. RBFOX2, LCK and SERBP1 were the hub proteins in the PPI network. In the TF-target gene network, RBFOX2, POU6F1, WIPF1 and PFKFB3 had the high connectivity with TFs. The expression status of 11 candidate DEGs was detected in GSE17755, the expression levels of MAT2A and NSA2 were significantly down-regulated and CD47 had the up-regulated tendency in peripheral blood cells of patients with RA compared with healthy individuals. qRT-PCR results of MAT2A, NSA2, CD47 were compatible with our bioinformatics analyses.DiscussionOur study might provide valuable information for exploring the pathogenesis mechanism of RA and identifying the potential biomarkers for RA diagnosis.
| S-EPMC5356478 | biostudies-literature
Project description:We aimed to identify key genes associated with rheumatoid arthritis (RA).The microarray datasets of GSE1919, GSE12021, and GSE21959 (35 RA samples and 32 normal controls) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) in RA samples were identified using the t test in limma package. Functional enrichment analysis was performed using clusterProfiler package. A protein-protein interaction (PPI) network of selected DEGs was constructed based on the Human Protein Reference Database. Active modules were explored using the jActiveModules plug-in in the Cytoscape Network Modeling package.In total, 537 DEGs in RA samples were identified, including 241 upregulated and 296 downregulated genes. A total of 24,451 PPI pairs were collected, and 5 active modules were screened. Furthermore, 19 submodules were acquired from the 5 active modules. Discs large homolog 1 (DLG1) and related DEGs such as guanylate cyclase 1, soluble, alpha 2 (GUCY1A2), N-methyl d-aspartate receptor 2A subunit (GRIN2A), and potassium voltage-gated channel member 1 (KCNA1) were identified in 8 submodules. Plasminogen (PLG) and related DEGs such as chemokine (C-X-C motif) ligand 2 (CXCL2), laminin, alpha 3 (LAMA3), complement component 7 (C7), and coagulation factor X (F10) were identified in 4 submodules.Our results indicate that DLG1, GUCY1A2, GRIN2A, KCNA1, PLG, CXCL2, LAMA3, C7, and F10 may play key roles in the progression of RA and may serve as putative therapeutic targets for treating RA.
| S-EPMC5626145 | biostudies-literature
Project description:Rheumatoid arthritis (RA) and osteoarthritis (OA) are the most common joint disorders. Although they have shown analogous clinical manifestations, the pathogenesis of RA and OA are different. In this study, we used the online Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE153015 to identify gene signatures between RA and OA joints. The relevant data on 8 subjects obtained from large joints of RA patients (RA-LJ), 8 subjects obtained from small joints of RA patients (RA-SJ), and 4 subjects with OA were investigated. Differentially expressed genes (DEGs) were screened. Functional enrichment analysis of DEGs including the Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were identified, which were mainly associated with T cell activation or chemokine activity. Besides, protein-protein interaction (PPI) network analysis was performed, and key modules were identified. Hub genes of RA-LJ and OA groups were screened, they were CD8A, GZMB, CCL5, CD2, and CXCL9, whereas CD8A, CD2, IL7R, CD27, and GZMB were hub genes of RA-SJ and OA group. The novel DEGs and functional pathways between RA and OA identified in this study may provide new insight into the underlying molecular mechanisms and therapeutic strategies of RA and OA.
| S-EPMC9968929 | biostudies-literature
Project description:MicroRNAs (miRNAs) act as epigenetic markers and regulate the expression of their target genes, including those characterized as regulators in autoimmune diseases. Rheumatoid arthritis (RA) is one of the most common autoimmune diseases. The potential roles of miRNA-regulated genes in RA pathogenesis have greatly aroused the interest of clinicians and researchers in recent years. In the current study, RA-related miRNAs records were obtained from PubMed through conditional literature retrieval. After analyzing the selected records, miRNA targeted genes were predicted. We identified 14 RA-associated miRNAs, and their sub-analysis in 5 microarray or RNA sequencing (RNA-seq) datasets was performed. The microarray and RNA-seq data of RA were also downloaded from NCBI Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA), analyzed, and annotated. Using a bioinformatics approach, we identified a series of differentially expressed genes (DEGs) by comparing studies on RA and the controls. The RA-related gene expression profile was thus obtained and the expression of miRNA-regulated genes was analyzed. After functional annotation analysis, we found GO molecular function (MF) terms significantly enriched in calcium ion binding (GO: 0005509). Moreover, some novel dysregulated target genes were identified in RA through integrated analysis of miRNA/mRNA expression. The result revealed that the expression of a number of genes, including ROR2, ABI3BP, SMOC2, etc., was not only affected by dysregulated miRNAs, but also altered in RA. Our findings indicate that there is a close association between negatively correlated mRNA/miRNA pairs and RA. These findings may be applied to identify genetic markers for RA diagnosis and treatment in the future.
| S-EPMC4567271 | biostudies-literature
Project description:The current study aimed to identify gene signatures during rheumatoid arthritis (RA) and osteoarthritis (OA), and used these to elucidate the underlying modular mechanisms. Using the Gene Expression Omnibus database, the present study obtained the GSE7669 mRNA expression microarray data from RA and OA synovial fibroblasts (n=6 each). The differentially expressed genes (DEGs) in RA synovial samples compared with OA samples were identified using the Linear Models for Microarray Analysis package. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using the Database for Annotation Visualization and Integrated Discovery. A protein‑protein interaction network was constructed and the modules were further analyzed using the Molecular Complex Detection plugin of Cytoscape. A total of 181 DEGs were identified by comparing RA and OA synovial samples (96 up‑ and 85 downregulated genes). The significant DEGs in module 1, including collagen, type I, α 1 (COL1A1), COL3A1, COL4A1 and COL11A1, were predominantly enriched in the extracellular matrix (ECM)‑receptor interaction and focal adhesion pathways. Additionally, significant DEGs in module 2, including radical S‑adenosyl methionine domain containing 2 (RSAD2), 2'‑5'‑oligoadenylate synthetase 2 (OAS2), myxovirus (influenza virus) resistance 1 (MX1) and ISG15 ubiquitin‑like modifier (ISG15), were predominantly associated with immune function pathways. In conclusion, the present study indicated that RSAD2, OAS2, MX1 and ISG15 may be notable gene signatures in RA development via regulation of the immune response. COL3A1, COL4A1, COL1A1 and COL11A1 may be important gene signatures in OA development via involvement in the pathways of ECM-receptor interactions and focal adhesions.
| S-EPMC4940106 | biostudies-literature
Project description:Sex differences have been suggested to play critical roles in the pathophysiology of osteoarthritis (OA), resulting in sex-specific prevalence and incidence. However, their roles in the development of OA remain largely unknown. The aim of this study was to screen out key genes and pathways mediating biological differences between OA females after menopause and OA males. First, the gene expression data of GSE36700 and GSE55457 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between sexes were identified using R software, respectively. The overlapping DEGs were obtained. Then, protein-protein interactive (PPI) network was constructed to further analyze interactions between the overlapping DEGs. Finally, enrichment analyses were separately performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes tools. In our results, a total of 278 overlapping DEGs were identified between OA females after menopause and OA males, including 219 upregulated and 59 downregulated genes. In the PPI network, seven hub genes were identified, including EGF, ERBB2, CDC42, PIK3R2, LCK, CBL, and STAT1. Functional enrichment analysis revealed that these genes were mainly enriched in PI3K-Akt signaling pathway, osteoclast differentiation, and focal adhesion. In conclusion, the results in the current study suggest that pathways of PI3K-Akt, osteoclast differentiation, and focal adhesion may play important roles in the development of OA females after menopause. EGFR, ERBB2, CDC42, and STAT1 may be key genes related to OA progression in postmenopausal women and may be promising therapeutic targets for OA.
| S-EPMC6925789 | biostudies-literature
Project description:The present study aimed to identify key genes involved in osteoarthritis (OA). Based on a bioinformatics analysis of five gene expression profiling datasets (GSE55457, GSE55235, GSE82107, GSE12021 and GSE1919), differentially expressed genes (DEGs) in OA were identified. Subsequently, a protein-protein interaction (PPI) network was constructed and its topological structure was analyzed. In addition, key genes in OA were identified following a principal component analysis (PCA) based on the DEGs in the PPI network. Finally, the functions and pathways enriched by these key genes were also analyzed. The PPI network consisted of 241 nodes and 576 interactives, including a total of 171 upregulated DEGs [e.g., aspartylglucosaminidase (AGA), CD58 and CD86] and a total of 70 downregulated DEGs (e.g., acetyl-CoA carboxylase β and dihydropyrimidine dehydrogenase). The PPI network complied with an attribute of scale-free small-world network. After PCA, 47 key genes were identified, including β-1,4-galactosyltransferase-1 (B4GALT1), AGA, CD58, CD86, ezrin, and eukaryotic translation initiation factor 4 γ 1 (EIF4G1). Subsequently, the 47 key genes were identified to be enriched in 13 Gene Ontology (GO) terms and 2 Kyoto Encyclopedia of Genes and Genomes pathways, with the GO terms involving B4GALT1 including positive regulation of developmental processes, protein amino acid terminal glycosylation and protein amino acid terminal N-glycosylation. In addition, B4GALT1 and EIF4G1 were confirmed to be downregulated in OA samples compared with healthy controls, but only EIF4G1 was determined to be significantly downregulated in OA samples, as determined via a meta-analysis of the 5 abovementioned datasets. In conclusion, B4GALT1 and EIF4G1 were indicated to have significant roles in OA, and B4GALT1 may be involved in positive regulation of developmental processes, protein amino acid terminal glycosylation and protein amino acid terminal N-glycosylation. The present study may enhance the current understanding of the molecular mechanisms of OA and provide novel therapeutic targets.
| S-EPMC7678638 | biostudies-literature
Project description:ObjectiveRheumatoid arthritis (RA) is one of the most prevalent inflammatory arthritis worldwide. However, the genes and pathways associated with macrophages from synovial fluids in RA patients still remain unclear. This study aims to screen and verify differentially expressed genes (DEGs) related to identifying candidate genes related to synovial macrophages in rheumatoid arthritis by bioinformatics analysis.MethodsWe searched the Gene Expression Omnibus (GEO) database, and GSE97779 and GSE10500 with synovial macrophages expression profiling from multiple RA microarray dataset were selected to conduct a systematic analysis. GSE97779 included nine macrophage samples from synovial fluids of RA patients and five macrophage samples from primary human blood of HC. GSE10500 included five macrophage samples from synovial fluids of RA patients and three macrophage samples from primary human blood of HC. Functional annotation of DEGs was performed, including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Protein-protein interaction (PPI) network of DEGs was established using the STRING database. CytoHubba was used to identify hub genes. MCODE was used to determine gene clusters in the interactive network.ResultsThere were 2638 DEGs (1425 upregulated genes and 1213 downregulated ones) and 889 DEGs (438 upregulated genes and 451 downregulated ones) selected from GSE97779 and GSE10500, respectively. Venn diagrams showed that 173 genes were upregulated and 106 downregulated in both two datasets. The top 10 hub genes, including FN1, VEGFA, HGF, SERPINA1, MMP9, PPBP, CD44, FPR2, IGF1, and ITGAM, were identified using the PPI network.ConclusionThis study provides new insights for the potential biomarkers and the relevant molecular mechanisms in RA patients. Our findings suggest that the 10 candidate genes might be used in diagnosis, prognosis, and therapy of RA in the future. However, further studies are required to confirm the expression of these genes in synovial macrophages in RA and control specimen.
| S-EPMC8575484 | biostudies-literature
Project description:Rheumatoid arthritis (RA) is a common autoimmune disease that can lead to severe joint damage and disability. And early diagnosis and treatment of RA can avert or substantially slow the progression of joint damage in up to 90% of patients, thereby preventing irreversible disability. Previous research indicated that 50% of the risk for the development of RA is attributable to genetic factors, but the pathogenesis is not well understood. Thus, it is urgent to identify biomarkers to arrest RA before joints are irreversibly damaged. Here, we first use the Robust Rank Aggregation method (RRA) to identify the differentially expressed genes (DEGs) between RA and normal samples by integrating four public RA patients' mRNA expression data. Subsequently, these DEGs were used as the input for the weighted gene co-expression network analysis (WGCNA) approach to identify RA-related modules. The function enrichment analysis suggested that the RA-related modules were significantly enriched in immune-related actions. Then the hub genes were defined as the candidate genes. Our analysis showed that the expression levels of candidate genes were significantly associated with the RA immune microenvironment. And the results indicated that the expression of the candidate genes can use as predictors for RA. We hope that our method can provide a more convenient approach for the early diagnosis of RA.
| S-EPMC9899220 | biostudies-literature
Project description:BackgroundOsteoarthritis (OA) is a common joint degenerative disease that can affect multiple joints. Genetic events may play an important regulatory role in the early stages of the disease, but the specific mechanisms have not yet been fully elucidated. The main purpose of this study was to screen for disease-causing hub genes and effective small molecule drugs to reveal the pathogenesis of OA and to develop novel drugs for treatment.MethodsTwo gene expression profile datasets, GSE55235 and GSE55457, were integrated and further analyzed. The consistently differentially expressed genes (DEGs) were identified, and functional annotation and pathway analysis of these genes were performed with GO and KEGG. A protein-protein interaction network (PPI) of the DEGs was generated using STRING, and potential small molecule drug screening was performed on the connectivity map (CMap).ResultsA total of 158 consistently differentially expressed genes were identified from the two profile datasets. The functions of these DEGs are mainly related to the TNF signaling pathway, osteoclast differentiation, MAPK signaling pathway and so on. The PPI network contains 127 nodes and 1802 edges, and the ten hub genes were interleukin 6 (IL6), vascular endothelial growth factor A (VEGFA)and so on. 7 small molecule drugs were identified as potential interactors with these hubs.ConclusionsThis study explains the disorder of expression in the pathological process of OA at transcriptome, which will help to understand the pathogenesis of OA.
| S-EPMC10009689 | biostudies-literature