Li Hongqiang, Hao Zhenyong, Zhao Liqiang, Liu Wei, Han Yanlong, Bai Yunxing, Wang Jian
Department of Orthopedics, The Harbin Fifth Hospital, Harbin, Heilongjiang 150001, P.R. China.
Mol Med Rep. 2016 Jun;13(6):4599-605. doi: 10.3892/mmr.2016.5144. Epub 2016 Apr 15.
The present study aimed to compare the molecular mechanisms of rheumatoid arthritis (RA) and osteoarthritis (OA). The microarray dataset no. GSE29746 was downloaded from Gene Expression Omnibus. After data pre‑processing, differential expression analysis between the RA group and the control, as well as between the OA group and the control was performed using the LIMMA package in R and differentially expressed transcripts (DETs) with |log2fold change (FC)|>1 and P<0.01 were identified. DETs screened from each disease group were then subjected to functional annotation using DAVID. Next, DETs from each group were used to construct individual interaction networks using the BIND database, followed by sub‑network mining using clusterONE. Significant functions of nodes in each sub‑network were also investigated. In total, 19 and 281 DETs were screened from the RA and OA groups, respectively, with only six common DETs. DETs from the RA and OA groups were enriched in 8 and 130 gene ontology (GO) terms, respectively, with four common GO terms, of which to were associated with phospholipase C (PLC) activity. In addition, DETs screened from the OA group were enriched in immune response‑associated GO terms, and those screened from the RA group were largely associated with biological processes linked with the cell cycle and chromosomes. Genes involved in PLC activity and its regulation were indicated to be altered in RA as well as in OA. Alterations in the expression of cell cycle‑associated genes were indicated to be linked with the occurrence of OA, while genes participating in the immune response were involved in the occurrence of RA.
本研究旨在比较类风湿关节炎(RA)和骨关节炎(OA)的分子机制。从基因表达综合数据库下载了编号为GSE29746的微阵列数据集。经过数据预处理后,使用R语言中的LIMMA软件包对RA组与对照组以及OA组与对照组之间进行差异表达分析,鉴定出|log2倍变化(FC)|>1且P<0.01的差异表达转录本(DET)。然后使用DAVID对从每个疾病组筛选出的DET进行功能注释。接下来,使用BIND数据库将每组的DET用于构建个体相互作用网络,随后使用clusterONE进行子网挖掘。还研究了每个子网中节点的重要功能。RA组和OA组分别筛选出19个和281个DET,只有6个共同的DET。RA组和OA组的DET分别富集在8个和130个基因本体(GO)术语中,有4个共同的GO术语,其中2个与磷脂酶C(PLC)活性相关。此外,从OA组筛选出的DET富集在与免疫反应相关的GO术语中,而从RA组筛选出的DET主要与细胞周期和染色体相关的生物学过程有关。参与PLC活性及其调节的基因在RA和OA中均发生改变。细胞周期相关基因表达的改变与OA的发生有关,而参与免疫反应的基因与RA的发生有关。