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首页> 外文期刊>Molecular medicine reports >Analysis of differentially expressed genes between rheumatoid arthritis and osteoarthritis based on the gene co-expression network
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Analysis of differentially expressed genes between rheumatoid arthritis and osteoarthritis based on the gene co-expression network

机译:基于基因共表达网络的类风湿关节炎与骨关节炎之间差异表达基因的分析

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摘要

The aim of the current study was to investigate disease-associated genes and related molecular mechanisms of osteoarthritis (OA) and rheumatoid arthritis (RA). Using GSE7669 datasets downloaded from Gene Expression Omnibus databases, the differentially expressed genes (DEGs) between RA and OA synovial fibroblasts (SFBs) (n=6 each) were screened. DEG-associated co-expression and topological properties were analyzed to determine the rank of disease-associated genes. Specifically, the fold change of differentially expressed genes, the clustering coefficient and the degree of differential gene co-expression were integrated to determine the disease-associated gene ranking. The underlying molecular mechanisms of these crucial disease-associated genes were investigated by gene ontology (GO) enrichment analysis. A total of 1313 DEGs, including 1068 upregulated genes and 245 downregulated genes were observed. The top 20 disease-associated genes were identified, including proteoglycan 4, inhibin ?B, carboxypeptidase M, alcohol dehydrogenase 1C and integrin ?. The major GO biological processes of these top 20 disease-associated genes were highly involved in the immune system, such as responses to stimuli, immune responses and inflammatory responses. This large-scale gene expression study observed disease-associated genes and their associated GO function in RA and OA, which may provide opportunities for biomarker development and novel insights into the molecular mechanisms of these two diseases.
机译:本研究的目的是研究骨关节炎(OA)和类风湿关节炎(RA)的疾病相关基因和相关分子机制。使用从Gene Expression Omnibus数据库下载的GSE7669数据集,筛选了RA和OA滑膜成纤维细胞(SFB)(n = 6)之间的差异表达基因(DEG)。分析与DEG相关的共表达和拓扑特性,以确定与疾病相关的基因的等级。具体而言,整合差异表达基因的倍数变化,聚类系数和差异基因共表达的程度,以确定与疾病相关的基因排名。通过基因本体论(GO)富集分析研究了这些关键的疾病相关基因的潜在分子机制。总共观察到1313个DEG,包括1068个上调的基因和245个下调的基因。鉴定出与疾病相关的前20个基因,包括蛋白聚糖4,抑制素βB,羧肽酶M,醇脱氢酶1C和整联蛋白β。这20种与疾病相关的基因的主要GO生物学过程与免疫系统高度相关,例如对刺激的反应,免疫反应和炎症反应。这项大规模的基因表达研究观察到了RA和OA中与疾病相关的基因及其相关的GO功能,这可能为生物标记物的开发和对这两种疾病的分子机制提供新的见解提供了机会。

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