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In this study, we also performed a systematic analysis of current OA gene Tubastatin A HCl data to identify DEGs in synovial tissues between OA and normal control (NC), which may be considered their utility as diagnostic markers. image Transcription factors (TFs) could enhance or inhibit gene transcription via binding to specific DNA sequences generally located in the promoter region of genes. Taking advantage of the resource of motif databases such as TRANSFAC and the results from integrated analysis of current gene expression data, we identified a set of TFs mediating gene expression in the process of OA pathogenesis, and constructed OA-specific transcriptional regulatory networks for a systematic understanding of disease progression at the molecular level. Hopefully, identification of crucial upstream regulators would provide clues to potential new therapeutic targets for the disease.
2. Methods
2.1. Identification of eligible OA gene expression datasets
Considering that OA was characterized by the breakdown of articular cartilage in synovial joints, we selected synovial gene expression profiling studies of OA on the Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov.bjdgm.cn/geo) [20]. Those datasets that were obtained from microarray experiments on the gene expression of synovial tissues in OA and NC, were downloaded.
2.2. Statistical analysis
The raw microarray datasets were downloaded, and preprocessed with log2 transformation and Z-score normalization for each study. The Linear Models for Microarray Data (Limma) package in R was used to identify the differently expressed genes between OA and controls by two-tailed Student\'s t-test, and P-value was obtained. Further false discovery rate (FDR) was calculated for multiple comparisons using the Benjamini & Hochberg method. Genes with FDR?<?0.01 were considered as differently expressed genes (DEGs). Hierarchical clustering analysis was performed using the ?heatmap.2? function of the R/Bioconductor package ?gplots? [21].<br />2.3. Functional annotation of DEGs
The biological functions of the DEGs in the pathogenesis of OA were interpreted by gene ontology (GO) enrichment analysis by GO-rilla, which is a web-accessible program for GO enrichment analysis [22]. GO provides functional annotation and classification for analyzing the gene sets data (i.e., biological process, cellular component, and molecular function). In order to understand the biological pathways that the DEGs were involved in, the Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis was also used. The web-based software GENECODIS [23] was used to perform pathway enrichment analysis, and it was considered to be statistically significant when a threshold P-value?<?0.05 was used in the hypergeometric test.<br />2.4. Construction of OA-specific transcriptional regulatory networks
TFs act as drivers or master regulators of gene expression to provide better clues to the underlying regulatory mechanisms. To provide a comprehensive and deeper knowledge about gene regulation underling OA, we extract information about TFs likely involved in regulating these DEGs. Based on OA signatures generated from integrated analysis, we searched TRANSFAC which is a database of TFs, their genomic binding sites, and DNA binding site sequence profiles for DEG coded TFs and their targeted genes, and used TRANSFAC position weight matrix (PWM) for gene promoter scanning [24] to identify DEGs which has the binding site of the TF in the promoter region. The transcriptional regulatory networks were visualized by Cytoscape [25].
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