Dataset Information

Identification of genes and pathways in the synovia of women with osteoarthritis by bioinformatics analysis.

ABSTRACT: Osteoarthritis (OA) has a high prevalence in female patients and sex may be a key factor affecting the progression of OA. The aim of the present study was to identify genetic signatures in the synovial membranes of female patients with OA and to elucidate the potential associated molecular mechanisms. The gene expression profiles of the GSE55457 and GSE55584 datasets were obtained from the Gene Expression Omnibus database. Data of two synovial membranes from normal female individuals (GSM1337306 and GSM1337310) and two synovial membranes from female patients affected by OA (GSM1337327 and GSM1337330) were obtained from the dataset GSE55457, and those of three synovial membranes from female patients affected by OA (GSM1339628, GSM1339629 and GSM1339632) were obtained from the dataset GSE55584. Differentially expressed genes (DEGs) were identified by using Morpheus software. Protein?protein interaction (PPI) networks of the DEGs were constructed by using Cytoscape software. Subsequently, Gene Ontology (GO) function and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses of the top module of the PPI network were performed by using ClueGo. A total of 377 DEGs were identified in the synovial membranes of OA patients compared with those of normal individuals, including 164 upregulated and 213 downregulated genes. The top 10 hub genes were ubiquitin (UB)C, ribosomal protein (RP) L23A, mammalian target of rapamycin, heat shock protein 90 ? family class A member 1, RPS28, RPL37A, RPS24, RPS4X, RPS18 and UBB. The results of the GO analysis indicated that the DEGs included in the top module of the PPI were mainly enriched in the terms 'nuclear?transcribed mRNA catabolic process', 'nonsense mediated decay', and 'cytoplasmic translation and ribosomal small subunit biogenesis'. KEGG pathway analysis indicated that the DEGs included in the top one module were mainly enriched in the 'ribosome' pathway. The present study provides a systematic, molecular?level understanding of the degeneration of the synovial membrane in the progression of OA in female patients. The hub genes and molecules associated with the synovial membrane may be used as biomarkers and therapeutic targets for the treatment of OA in female patients with OA.

SUBMITTER: Mi B

PROVIDER: S-EPMC5802222 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Identification of genes and pathways in the synovia of women with osteoarthritis by bioinformatics analysis.

Mi Bobin B Liu Guohui G Zhou Wu W Lv Huijuan H Liu Yi Y Liu Jing J

Molecular medicine reports 20180115 3

Osteoarthritis (OA) has a high prevalence in female patients and sex may be a key factor affecting the progression of OA. The aim of the present study was to identify genetic signatures in the synovial membranes of female patients with OA and to elucidate the potential associated molecular mechanisms. The gene expression profiles of the GSE55457 and GSE55584 datasets were obtained from the Gene Expression Omnibus database. Data of two synovial membranes from normal female individuals (GSM1337306 ...[more]

PMID: 29344651

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Project description:Osteoarthritis (OA) is a chronic arthropathy that occurs in the middle‑aged and elderly population. The present study aimed to identify gene signature differences between synovial cells from OA synovial membrane with and without inflammation, and to explain the potential mechanisms involved. The differentially expressed genes (DEGs) between 12 synovial membrane with inflammation and 12 synovial membrane without inflammation from the dataset GSE46750 were identified using the Gene Expression Omnibus 2R. The DEGs were subjected to enrichment analysis, protein‑protein interaction (PPI) analysis and module analysis. The analysis results were compared with text‑mining results. A total of 174 DEGs were identified. Gene Ontology enrichment results demonstrated that functional molecules encoded by the DEGs primarily had extracellular location, molecular functions predominantly involving 'chemokine activity' and 'cytokine activity', and were associated with biological processes, including 'inflammatory response' and 'immune response'. The Kyoto Encyclopedia of Genes and Genomes results demonstrated that DEGS may function through pathways associated with 'rheumatoid arthritis', 'chemokine signaling pathway', 'complement and coagulation cascades', 'TNF signaling pathway', 'intestinal immune networks for IgA production', 'cytokine‑cytokine receptor interaction', 'allograft rejection', 'Toll‑like receptor signaling pathway' and 'antigen processing and presentation'. The top 10 hub genes [interleukin (IL)6, IL8, matrix metallopeptidase (MMP)9, colony stimulating factor 1 receptor, FOS proto‑oncogene, AP1 transcription factor subunit, insulin‑like growth factor 1, TYRO protein tyrosine kinase binding protein, MMP3, cluster of differentiation (CD)14 and CD163] and four gene modules were identified from the PPI network using Cytoscape. In addition, text‑mining was used to identify the commonly used drugs and their targets for the treatment of OA. It was initially verified whether the results of the present study were useful for the study of OA treatment targets and pathways. The present study provided insight for the molecular mechanisms of OA synovitis. The hub genes and associated pathways derived from analysis may be targets for OA treatment. IL8 and MMP9, which were validated by text‑mining, may be used as molecular targets for the OA treatment, while other hub genes require further validation.

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.

Project description:Esophageal adenocarcinoma (EAC) is one of the most common subtypes of esophageal cancer, and is associated with a low 5-year survival rate. The present study aimed to identify key genes and pathways associated with EAC using bioinformatics analysis. The gene expression profiles of GSE92396, which includes 12 EAC samples and 9 normal esophageal samples, were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between the EAC and normal samples were identified using the limma package in R language. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the identified DEGs were conducted using the online analysis tool, the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software. Finally, module analysis was conducted for the PPI network using the MCODE plug-in in Cytoscape. Of the 386 DEGs identified, the 150 upregulated genes were mainly enriched in the KEGG pathways of complement and coagulation cascades, maturity onset diabetes of the young and protein digestion and absorption; and the 236 downregulated genes were mainly enriched in amoebiasis, retinol metabolism and drug metabolism-cytochrome P450. Based on information from the STRING database, a PPI network comprising of 369 nodes and 534 edges was constructed in Cytoscape. The top 10 hub nodes with the highest degrees were determined as interleukin-8, involucrin, tissue inhibitor of metalloproteinase 1, fibronectin 1, serpin family E member 1, serpin family A member 1, cystic fibrosis transmembrane conductance regulator, secreted phosphoprotein 1, collagen type I alpha 1 chain and angiotensinogen. A total of 6 modules were detected from the PPI network that satisfied the criteria of MCODE score >4 and number of nodes >4. KEGG pathways enriched for the module DEGs were mainly within arachidonic acid metabolism, complement and coagulation cascades and rheumatoid arthritis. In conclusion, identification of these key genes and pathways may improve understanding of the mechanisms underlying the development of EAC, and may be used as diagnostic and therapeutic targets in EAC.

Dataset Information

Identification of genes and pathways in the synovia of women with osteoarthritis by bioinformatics analysis.

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Identification of genes and pathways in the synovia of women with osteoarthritis by bioinformatics analysis.

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