Gu Song, Li Guanghui, Zhang Xitao, Yan Jun, Gao Jie, An Xiangguang, Liu Yan, Su Pixiong
Department of Cardiac Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, P.R. China.
Mol Med Rep. 2015 Apr;11(4):2631-43. doi: 10.3892/mmr.2014.3102. Epub 2014 Dec 17.
Chronic thromboembolic pulmonary hypertension (CTEPH) is one of the primary causes of severe pulmonary hypertension. In order to identify long noncoding RNAs (lncRNAs) that may be involved in the development of CTEPH, comprehensive lncRNA and messenger RNA (mRNA) profiling of endothelial tissues from the pulmonary arteries of CTEPH patients was conducted with microarray analysis. Differential expression of 185 lncRNAs was observed in the CTEPH tissues compared with healthy control tissues. Further analysis identified 464 regulated enhancer‑like lncRNAs and overlapping, antisense or nearby mRNA pairs. Coexpression networks were subsequently constructed and investigated. The expression levels of the lncRNAs, NR_036693, NR_027783, NR_033766 and NR_001284, were significantly altered. Gene ontology and pathway analysis demonstrated the potential role of lncRNAs in the regulation of central process, including inflammatory response, response to endogenous stimulus and antigen processing and presentation. The use of bioinformatics may help to uncover and analyze large quantities of data identified by microarray analyses, through rigorous experimental planning, statistical analysis and the collection of more comprehensive data regarding CTEPH. The results of the present study provided evidence which may be helpful in future studies on the diagnosis and management of CTEPH.
慢性血栓栓塞性肺动脉高压（CTEPH）是严重肺动脉高压的主要病因之一。为了鉴定可能参与CTEPH发生发展的长链非编码RNA（lncRNA），采用微阵列分析对CTEPH患者肺动脉内皮组织进行了全面的lncRNA和信使RNA（mRNA）谱分析。与健康对照组织相比，在CTEPH组织中观察到185种lncRNA的差异表达。进一步分析确定了464种受调控的增强子样lncRNA以及重叠、反义或附近的mRNA对。随后构建并研究了共表达网络。lncRNA NR_036693、NR_027783、NR_033766和NR_001284的表达水平发生了显著改变。基因本体论和通路分析表明lncRNA在调控包括炎症反应、对内源性刺激的反应以及抗原加工和呈递在内的核心过程中具有潜在作用。生物信息学的应用可能有助于通过严格的实验设计、统计分析以及收集关于CTEPH更全面的数据来揭示和分析微阵列分析鉴定出的大量数据。本研究结果为未来CTEPH的诊断和管理研究提供了可能有用的证据。
