生物
竞争性内源性RNA
微阵列分析技术
微阵列
长非编码RNA
计算生物学
小RNA
生物信息学
基因表达
核糖核酸
遗传学
基因
作者
Liang Zhang,Li Yang,Wenhui Li,Yalin Yang,Weizong Sun,Pengfei Gong,Ling Wang,Kai Wang
出处
期刊:Genomics
[Elsevier BV]
日期:2019-01-05
卷期号:111 (6): 1192-1200
被引量:6
标识
DOI:10.1016/j.ygeno.2019.01.005
摘要
It has been reported that a wide range of long non-coding RNAs (lncRNAs) are implicated in numerous diseases such as tumor, cardiopathy and neurological disorders. Identifying the differentially expressed (DE) profile of lncRNAs in cervical spondylotic myelopathy (CSM) is essential to understand the mechanisms of CSM.Microarray assay, quantitative real-time PCR (qRT-PCR) and bioinformatics analysis were employed to reveal the DE profile and potential functions of lncRNAs in CSM.Microarray analysis displayed the DE profiles of lncRNAs and mRNAs in rats between the CSM group and the control (CON) group. Thereinto, 1266 DE lncRNAs (738 up-regulation and 528 down-regulation) and 847 mRNAs (487 up-regulation and 360 down-regulation) with >1.1 fold change (FC) were finally identified. Moreover, 17 lncRNAs (13 up-regulation and 4 down-regulation) and 18 mRNAs (13 up-regulation and 5 down-regulation) were found deregulated by >2 FC. Further bioinformatics analysis showed the most remarkable biological processes among up-regulated RNAs contain cellular response to interferon-beta, inflammatory response and innate immune response, which may involve in CSM. Besides, related DE mRNAs of 17 DE lncRNAs in the genome were related to signaling pathway about NOD-like receptor, TNF, and apoptosis. In addition, a co-expression network of lncRNA-mRNA was established for analyzing the biological roles of lncRNAs. Among these, we found a ceRNA network related to CSM. Finally, the expressions of the DE lncRNAs and ceRNA network confirmed by qRT-PCR were in agreement with microarray data.Our study revealed the DE profiles of lncRNAs and mRNAs for CSM. Those dysregulated RNAs may represent potential therapeutic targets of CSM for further study.
科研通智能强力驱动
Strongly Powered by AbleSci AI