Department of Anesthesiology, Shouguang People Hospital, Weifang, 262700, China.
Mol Pain. 2017 Jan-Dec;13:1744806917736973. doi: 10.1177/1744806917736973.
The aim of this study was to predict key genes and their relationships for anxiety and nociceptive sensitivity related to Comt1 genetype.
: The raw data of E-GEOD-20160 related to anxiety and nociceptive sensitivity were obtained. Pearson correlation coefficient of interaction in protein–protein interaction was calculated. Topological analysis was processed for protein– protein interaction network, and genes in this network were ranked based on their degrees. Ego genes were identified, and models were searched and refined. A total of 1000 randomized tests were processed for ego networks. The classification accuracy of each ego network was obtained in this process.
The interactions with genes in gene expression profiles were extracted, and protein–protein interaction was constructed. The protein–protein interaction included 4639 genes and 43,837 relationships. Differential co-expression network was constructed, and 74 ego genes were obtained. Thereinto, top five ego genes were ADCY2, GRM8, S1PR3, ADCY6, and ANXA1. After module searching and refinement, a total of 11 candidate modules were obtained, including module 14, module 51, and module 9. In addition, these 11 modules were confirmed to be with significance. Module 14 contained 10 genes, such as HRH3, DRD2, and CXCR3. Similarly, module 51 included six genes, such as HELZ2, NCOA3, and MED30.
Ego network analysis was a useful and comprehensive method for biomarkers screening. Several modules such as module 3 and module 36 were important subnetworks. Potential genes in these modules including ADCYs, GNAI1, DRD2, PNOC, CCR2, DRD2, and LPAR1 might be important genes in the research of anxiety and nociceptive sensitivity.
本研究旨在预测与 Comt1 基因型相关的焦虑和伤害感受敏感性的关键基因及其关系。
获取与焦虑和伤害感受敏感性相关的 E-GEOD-20160 的原始数据。计算蛋白质-蛋白质相互作用的皮尔逊相关系数。对蛋白质-蛋白质相互作用网络进行拓扑分析，并根据节点度对网络中的基因进行排序。识别自我基因，并搜索和优化模型。对 1000 次随机测试进行 ego 网络处理，获得每个 ego 网络的分类准确率。
提取基因表达谱中与基因相互作用的基因，并构建蛋白质-蛋白质相互作用网络。该蛋白质-蛋白质相互作用网络包括 4639 个基因和 43837 个关系。构建差异共表达网络，得到 74 个自我基因。其中，前五个自我基因分别为 ADCY2、GRM8、S1PR3、ADCY6 和 ANXA1。经过模块搜索和优化，共得到 11 个候选模块，包括模块 14、模块 51 和模块 9。此外，这些 11 个模块均被证实具有显著意义。模块 14 包含 10 个基因，如 HRH3、DRD2 和 CXCR3。同样，模块 51 包含 6 个基因，如 HELZ2、NCOA3 和 MED30。
自我网络分析是一种筛选生物标志物的有用且全面的方法。模块 3 和模块 36 等几个模块是重要的子网。这些模块中的潜在基因，包括 ADCYs、GNAI1、DRD2、PNOC、CCR2、DRD2 和 LPAR1，可能是焦虑和伤害感受敏感性研究中的重要基因。
