Chen Xiao-Min, Feng Ming-Jun, Shen Cai-Jie, He Bin, Du Xian-Feng, Yu Yi-Bo, Liu Jing, Chu Hui-Min
Department of Cardiology, Ningbo First Hospital, Ningbo, Zhejiang 315000, P.R. China.
Department of Cardiology, Ningbo Seventh Hospital, Ningbo, Zhejiang 315000, P.R. China.
Mol Med Rep. 2017 Jul;16(1):773-777. doi: 10.3892/mmr.2017.6667. Epub 2017 May 31.
The present study was designed to develop a novel method for identifying significant pathways associated with human hypertrophic cardiomyopathy (HCM), based on gene co‑expression analysis. The microarray dataset associated with HCM (E‑GEOD‑36961) was obtained from the European Molecular Biology Laboratory‑European Bioinformatics Institute database. Informative pathways were selected based on the Reactome pathway database and screening treatments. An empirical Bayes method was utilized to construct co‑expression networks for informative pathways, and a weight value was assigned to each pathway. Differential pathways were extracted based on weight threshold, which was calculated using a random model. In order to assess whether the co‑expression method was feasible, it was compared with traditional pathway enrichment analysis of differentially expressed genes, which were identified using the significance analysis of microarrays package. A total of 1,074 informative pathways were screened out for subsequent investigations and their weight values were also obtained. According to the threshold of weight value of 0.01057, 447 differential pathways, including folding of actin by chaperonin containing T‑complex protein 1 (CCT)/T‑complex protein 1 ring complex (TRiC), purine ribonucleoside monophosphate biosynthesis and ubiquinol biosynthesis, were obtained. Compared with traditional pathway enrichment analysis, the number of pathways obtained from the co‑expression approach was increased. The results of the present study demonstrated that this method may be useful to predict marker pathways for HCM. The pathways of folding of actin by CCT/TRiC and purine ribonucleoside monophosphate biosynthesis may provide evidence of the underlying molecular mechanisms of HCM, and offer novel therapeutic directions for HCM.
本研究旨在基于基因共表达分析开发一种新方法,以识别与人类肥厚型心肌病(HCM)相关的重要信号通路。与HCM相关的微阵列数据集(E-GEOD-36961)取自欧洲分子生物学实验室-欧洲生物信息学研究所数据库。基于Reactome信号通路数据库和筛选处理选择信息丰富的信号通路。采用经验贝叶斯方法构建信息丰富信号通路的共表达网络,并为每条信号通路赋予一个权重值。基于权重阈值提取差异信号通路,该阈值使用随机模型计算。为了评估共表达方法是否可行,将其与使用微阵列显著性分析软件包鉴定的差异表达基因的传统信号通路富集分析进行比较。总共筛选出1074条信息丰富的信号通路用于后续研究,并获得了它们的权重值。根据权重值阈值0.01057,获得了447条差异信号通路,包括含T-复合体蛋白1(CCT)/T-复合体蛋白1环复合体(TRiC)的伴侣蛋白介导的肌动蛋白折叠、嘌呤核糖核苷单磷酸生物合成和泛醇生物合成。与传统的信号通路富集分析相比,共表达方法获得的信号通路数量有所增加。本研究结果表明,该方法可能有助于预测HCM的标志性信号通路。CCT/TRiC介导的肌动蛋白折叠和嘌呤核糖核苷单磷酸生物合成信号通路可能为HCM的潜在分子机制提供证据,并为HCM提供新的治疗方向。