Investigation of candidate genes for osteoarthritis based on gene expression profiles.
ABSTRACT: OBJECTIVE:To explore the mechanism of osteoarthritis (OA) and provide valid biological information for further investigation. METHODS:Gene expression profile of GSE46750 was downloaded from Gene Expression Omnibus database. The Linear Models for Microarray Data (limma) package (Bioconductor project, http://www.bioconductor.org/packages/release/bioc/html/limma.html) was used to identify differentially expressed genes (DEGs) in inflamed OA samples. Gene Ontology function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were performed based on Database for Annotation, Visualization and Integrated Discovery data, and protein-protein interaction (PPI) network was constructed based on the Search Tool for the Retrieval of Interacting Genes/Proteins database. Regulatory network was screened based on Encyclopedia of DNA Elements. Molecular Complex Detection was used for sub-network screening. Two sub-networks with highest node degree were integrated with transcriptional regulatory network and KEGG functional enrichment analysis was processed for 2 modules. RESULTS:In total, 401 up- and 196 down-regulated DEGs were obtained. Up-regulated DEGs were involved in inflammatory response, while down-regulated DEGs were involved in cell cycle. PPI network with 2392 protein interactions was constructed. Moreover, 10 genes including Interleukin 6 (IL6) and Aurora B kinase (AURKB) were found to be outstanding in PPI network. There are 214 up- and 8 down-regulated transcription factor (TF)-target pairs in the TF regulatory network. Module 1 had TFs including SPI1, PRDM1, and FOS, while module 2 contained FOSL1. The nodes in module 1 were enriched in chemokine signaling pathway, while the nodes in module 2 were mainly enriched in cell cycle. CONCLUSION:The screened DEGs including IL6, AGT, and AURKB might be potential biomarkers for gene therapy for OA by being regulated by TFs such as FOS and SPI1, and participating in the cell cycle and cytokine-cytokine receptor interaction pathway.
Project description:Osteoarthritis (OA) is one of the most common diseases worldwide, but the pathogenic genes and pathways are largely unclear. The aim of this study was to screen and verify hub genes involved in OA and explore potential molecular mechanisms. The expression profiles of GSE12021 and GSE55235 were downloaded from the Gene Expression Omnibus (GEO) database, which contained 39 samples, including 20 osteoarthritis synovial membranes and 19 matched normal synovial membranes. The raw data were integrated to obtain differentially expressed genes (DEGs) and were deeply analyzed by bioinformatics methods. The Gene Ontology (GO) and pathway enrichment of DEGs were performed by DAVID and Kyoto Encyclopedia of Genes and Genomes (KEGG) online analyses, respectively. The protein-protein interaction (PPI) networks of the DEGs were constructed based on data from the STRING database. The top 10 hub genes VEGFA, IL6, JUN, IL1β, MYC, IL4, PTGS2, ATF3, EGR1, and DUSP1 were identified from the PPI network. Module analysis revealed that OA was associated with significant pathways including TNF signaling pathway, cytokine-cytokine receptor interaction, and osteoclast differentiation. The qRT-PCR result showed that the expression level of IL6, VEGFA, JUN, IL-1β, and ATF3 was significantly increased in OA samples (p < 0.05), and these candidate genes could be used as potential diagnostic biomarkers and therapeutic targets of OA.
Project description:The current study aimed to explore the mechanisms associated with classic Hodgkin lymphoma (cHL) to identify novel diagnostic and therapeutic targets. The GES12453 microarray dataset was downloaded from the Gene Expression Omnibus database; the differentially expressed genes (DEGs) between cHL samples and normal B cell samples by were identified using the limma package. Gene ontology (GO) and pathway enrichment analysis of DEGs gene were performed. Furthermore, construction and analysis of protein?protein interaction (PPI) network was performed, and co?expression modules of DEGs were produced. A total of 450 DEGs were identified, comprising 216 upregulated and 234 downregulated genes in cHL compared with normal B cell samples. The DEGs were enriched in biological processes associated with immune response. The upregulated genes were mainly associated with the pathway of transcriptional misregulation in cancer, while downregulated genes were associated with B cell receptor signaling. PPI network analysis demonstrated that IL6 had the highest connectivity degree. Interleukin?6 (IL6) and signal transducer and activator of transcription 1 (STAT1) were demonstrated to be involved with the response to cytokine GO term in co?expression module 1. Spleen tyrosine kinase (SYK), B?cell linker protein (BLNK), CD79B, phospholipase C ?2 (PLCG2) were enriched in the B cell receptor signaling pathway in module 2. Matrix metallopeptidase 9 (MMP9), protein tyrosine phosphatase receptor type C had the highest connectivity degrees in module 3 and module 4, respectively. The results suggested that DEGs, including IL6, STAT1, MMP9, SYK, BLNK, PLCG2 and CD79B, and the pathways of B cell receptor signaling, Epstein?Barr virus infection and transcriptional misregulation in cancer have strong potential to be useful as targets for diagnosis or treatment of cHL.
Project description:BACKGROUND:Osteoarthritis (OA) is the most common chronic disorder of joints; however, the key genes and transcription factors (TFs) associated with OA are still unclear. Through bioinformatics tools, the study aimed to understand the mechanism of genes associated with the development of OA. METHODS:Four gene expression profiling datasets were used to identify differentially expressed genes (DEGs) between OA and healthy control samples by a meta-analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed with Multifaceted Analysis Tool for Human Transcriptome (MATHT). Subsequently, a protein-protein interaction (PPI) network was constructed for these DEGs. Significant network modules were identified using ReactomeFIViz, and the pathway of each module was enriched using MATHT. In addition, TFs in the DEGs were identified. RESULTS:In total, 690 DEGs were identified between OA and healthy control samples, including 449 upregulated and 241 downregulated DEGs. Additionally, 622 nodes and 2752 interactions constituted the PPI network, including 401 upregulated and 221 downregulated DEGs. Among them, FOS, TWIST1, POU2F1, SMARCA4, and CREBBP were also identified as TFs. RT-PCR results showed that the expression levels of Fos, Twist1, Pou2f1, Smarca4, and Crebbp decreased in mice with OA. In addition, FOS, TWIST1, SMARCA4, and CREBBP were involved in the positive regulation of transcription from the RNA polymerase II promoter. CONCLUSIONS:TWIST1, POU2F1, SMARCA4, and CREBBP may play an important role in OA pathology.
Project description:BACKGROUND:The study aimed to identify the targeting genes and miRNAs using the microarray expression profile dataset for Osteoarthritis (OA) patients. Differentially expressed genes (DEGs) between OA and control samples were identified using Bayes method of limma package. Subsequently, a protein-protein interaction (PPI) network was constructed. miRNAs and transcription factor (TFs) based on DEGs in PPI network were identified using Webgestalt and ENCODE, respectively. Finally, MCODE, Gene Ontology (GO) function, and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed. The expressions of several DEGs and predicted miRNAs in OA rats were detected by RT-PCR. RESULTS:A total of 594 DEGs were identified. In PPI network, there were 313 upregulated DEGs and 22 downregulated DEGs. Besides, the regulatory relationships included 467 upregulated interactions and 85 downregulated interactions (miR-124A???QKI and MAP 1B) between miRNA and DEGs in PPI network. The module from downregulated DEGs-TFs-miRNA networks was mainly enriched to low-density lipoprotein particle clearance, response to linoleic acid, and small molecule metabolic process BP terms. Moreover, QKI, MAP 1B mRNA and miR-9 expressions were significantly reduced in OA rats. CONCLUSION:miR-9 might be a protective factor for OA patients via inhibiting proliferation and differentiation of cartilage progenitor cells. miR-124A might play an important role in progression of OA through targeting QKI and MAP 1B.
Project description:The aim of this study was to explore epilepsy-related mechanism so as to figure out the possible targets for epilepsy treatment.The gene expression profile dataset GES32534 was downloaded from Gene Expression Omnibus database. We identified the differentially expressed genes (DEGs) by Affy package. Then the DEGs were used to perform gene ontology (GO) and pathway enrichment analyses. Furthermore, a protein-protein interaction (PPI) network was constructed with the DEGs followed by co-expression modules construction and analysis.Total 420 DEGs were screened out, including 214 up-regulated and 206 down-regulated genes. Functional enrichment analysis revealed that down-regulated genes were mainly involved in the process of immunity regulation and biological repairing process while up-regulated genes were closely related to transporter activity. PPI network analysis showed the top ten genes with high degrees were all down-regulated, among which FN1 had the highest degree. The up-regulated and down-regulated DEGs in the PPI network generated two obvious sub-co-expression modules, respectively. In up-co-expression module, SCN3B (sodium channel, voltage gated, type III beta subunit) was enriched in GO:0006814 ~ sodium ion transport. In down-co-expression module, C1QB (complement C1s), C1S (complement component 1, S subcomponent) and CFI (complement factor I) were enriched in GO:0006955 ~ immune response.The immune response and complement system play a major role in the pathogenesis of epilepsy. Additionally, C1QB, C1S, CFI, SCN3B and FN1 may be potential therapeutic targets for epilepsy.
Project description: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 α 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.
Project description:Synovial sarcoma (SS) is a highly aggressive soft tissue tumor with high risk of local recurrence and metastasis. However, the mechanisms underlying SS metastasis are still largely unclear. The purpose of this study is to screen metastasis-associated biomarkers in SS by integrated bioinformatics analysis. Two mRNA datasets (GSE40018 and GSE40021) were selected to analyze the differentially expressed genes (DEGs). Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), functional and pathway enrichment analyses were performed for DEGs. Then, the protein-protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. The module analysis of the PPI network and hub genes validation were performed using Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the hub genes were performed using WEB-based GEne SeT AnaLysis Toolkit (WebGestalt). The expression levels and survival analysis of hub genes were further assessed through Gene Expression Profiling Interactive Analysis (GEPIA) and the Kaplan-Meier plotter database. In total, 213 overlapping DEGs were identified, of which 109 were upregulated and 104 were downregulated. GO analysis revealed that the DEGs were predominantly involved in mitosis and cell division. KEGG pathways analysis demonstrated that most DEGs were significantly enriched in cell cycle pathway. GSEA revealed that the DEGs were mainly enriched in oocyte meiosis, cell cycle and DNA replication pathways. A key module was identified and 10 hub genes (CENPF, KIF11, KIF23, TTK, MKI67, TOP2A, CDC45, MELK, AURKB, and BUB1) were screened out. The expression and survival analysis disclosed that the 10 hub genes were upregulated in SS patients and could result in significantly reduced survival. Our study identified a series of metastasis-associated biomarkers involved in the progression of SS, and may provide novel therapeutic targets for SS metastasis.
Project description:Gastric adenocarcinoma (GAC), also known as stomach adenocarcinoma (STAD), is one of the most lethal malignancies in the world. It is vital to classify and detect the hub genes and key pathways participated in the initiation and progression of GAC. In this study, we collected and sequenced 15 pairs of GAC tumor tissues and the adjacent normal tissues. Differentially expressed genes (DEGs) were analyzed and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) analysis were used to annotate the unique biological significance and important pathways of enriched DEGs. Moreover, we constructed the protein-protein interaction (PPI) network by Cytoscape and conducted KEGG enrichment analysis of the prime module. We further applied the TCGA database to start the survival analysis of these hub genes by Kaplan-Meier estimates. Finally, we obtained total 233 DEGs consisted of 64 up-regulated genes and 169 down-regulated genes. GO enrichment analysis found that DEGs most significantly enriched in single organism process, extracellular region, and extracellular region part. KEGG pathway enrichment analysis suggested that DEGs most significantly enriched in Protein digestion and absorption, Gastric acid secretion, and ECM-receptor interaction. Furthermore, the PPI network showed that the top 10 hub genes in GAC were IL8, COL1A1, MMP9, SST, COL1A2, TIMP1, FN1, SPARC, ALDH1A1, and SERPINE1 respectively. The prime gene interaction module in PPI network was enriched in protein digestion and absorption, ECM receptor interaction, the PI3K-Akt signaling pathway, and pathway in cancer. Survival analysis based on the TCGA database found that the expression of the FN1, SERPINE1, and SPARC significantly predicted poor prognosis of GAC. Collectively, we identified several hub genes and key pathways associated with GAC initiation and progression by analyzing the microarray data on DEGs, which provided a detailed molecular mechanism underlying GAC occurrence and progression.
Project description:BACKGROUND Spinal cord injury (SCI) is the most critical complication of spinal injury. We aimed to identify differentially expressed genes (DEGs) and to find associated pathways that may function as targets for SCI prognosis and therapy. MATERIAL AND METHODS Seven gene microarray expression profiles, downloaded from the GEO database (ID: GSE33886), were used to screen the DEGs of leg tissue and to compare these between SCI patients and corresponding normal specimens. Then, GO enrichment analysis was performed on these selected DEGs. Afterwards, interactions among these DEGs were analyzed by String database and then a PPI network was constructed to obtain topology character and modules in the PPI network. Finally, roles of the critical proteins in the pathway were explained by comparing the enrichment results of the genes in sub-modules and all the DEGs. RESULTS A total of 113 DEGs were determined. We found that 21 up-regulated genes were enriched in 7 biological processes, while 9 down-regulated genes were significantly enriched in 4 KEGG pathways. The PPI network was constructed, including 40 interacting genes and 73 interactions. Three obvious function modules were identified by exploring the PPI network, and ACTC1 was identified as the critical protein in the 3 enriched signal pathways. However, no obvious difference was found in the signal pathway in which both the 11 genes in module 1 and all 113 DEGs participated. CONCLUSIONS Core proteins in the signal pathway associated with spinal cord injury may serve as potential prognostic and predictive markers for the diagnosis and treatment of spinal cord injury in clinical applications.
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.