Liu Mu, Luo Ming, Sun Haoliang, Ni Buqing, Shao Yongfeng
The First Medical School of Nanjing Medical University, Nanjing Medical University.
School of the Basic Medical Sciences, Nanjing Medical University.
Tohoku J Exp Med. 2017 Dec;243(4):263-273. doi: 10.1620/tjem.243.263.
In our aging world, increasing numbers of people are suffering from calcific aortic valve disease (CAVD). In this study, we used integrated bioinformatics analysis to predict several key genes that are involved in the initiation and progression of CAVD. Expression profiles of 15 calcific and 14 normal human aortic valve samples were generated from two gene expression datasets (GSE12644 and GSE51472). Dataset GSE26953 from the human aortic valve fibrosa-derived endothelial cells cultured under laminar or oscillatory shear stress was also evaluated. Related R packages were used to process the data. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for functional annotation. Hub genes were identified based on the protein-protein interaction network. CAVD-related gene modules were identified by Weighted Gene Co-expression Network Analysis (WGCNA). The predicted key genes were manually reviewed. In our present work, complex connections among mechano-response, oxidative stress, inflammation and extracellular remodeling pathways in the etiology of CAVD were revealed. The key genes, thus identified, encode a transcription factor KLF2 and phospholipid phosphatase 3 (PLPP3) that are involved in mechano-responses; eNOS involved in oxidative stress; IL-8 involved in inflammation; and collagen triple helix repeat containing 1 (CTHRC1) and secretogranin II (SCG2) involved in extracellular remodeling. These gene products are predicted to play critical roles in CAVD development and progression. The present study provides valuable information for future research and drug development.
在我们这个老龄化的世界中，越来越多的人正遭受钙化性主动脉瓣疾病（CAVD）的折磨。在本研究中，我们运用综合生物信息学分析来预测一些参与CAVD发生和发展的关键基因。从两个基因表达数据集（GSE12644和GSE51472）生成了15个钙化的和14个正常的人类主动脉瓣样本的表达谱。还评估了来自在层流或振荡剪切应力下培养的人主动脉瓣纤维层衍生内皮细胞的数据集GSE26953。使用相关的R包来处理数据。进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）通路富集分析以进行功能注释。基于蛋白质 - 蛋白质相互作用网络鉴定枢纽基因。通过加权基因共表达网络分析（WGCNA）鉴定与CAVD相关的基因模块。对预测的关键基因进行人工审查。在我们目前的工作中，揭示了CAVD病因中机械反应、氧化应激、炎症和细胞外重塑途径之间的复杂联系。由此鉴定出的关键基因编码参与机械反应的转录因子KLF2和磷脂磷酸酶3（PLPP3）；参与氧化应激的eNOS；参与炎症的IL - 8；以及参与细胞外重塑的含胶原三螺旋重复序列1（CTHRC1）和分泌粒蛋白II（SCG2）。预计这些基因产物在CAVD的发生和发展中起关键作用。本研究为未来的研究和药物开发提供了有价值的信息。
