Song Xian-Dong, Song Xian-Xu, Liu Gui-Bo, Ren Chun-Hui, Sun Yuan-Bo, Liu Ke-Xin, Liu Bo, Liang Shuang, Zhu Zhu
Department of Orthopaedics, Hongqi Hospital of Mudanjiang Medical University, Mudanjiang 157000, Heilongjiang, People's Republic of China.
J Genet. 2018 Mar;97(1):173-178.
The traditional methods of identifying biomarkers in rheumatoid arthritis (RA) have focussed on the differentially expressed pathways or individual pathways, which however, neglect the interactions between pathways. To better understand the pathogenesis of RA, we aimed to identify dysregulated pathway sets using a pathway interaction network (PIN), which considered interactions among pathways. Firstly, RA-related gene expression profile data, protein-protein interactions (PPI) data and pathway data were taken up from the corresponding databases. Secondly, principal component analysis method was used to calculate the pathway activity of each of the pathway, and then a seed pathway was identified using data gleaned from the pathway activity. A PIN was then constructed based on the gene expression profile, pathway data, and PPI information. Finally, the dysregulated pathways were extracted from the PIN based on the seed pathway using the method of support vector machines and an area under the curve (AUC) index. The PIN comprised of a total of 854 pathways and 1064 pathway interactions. The greatest change in the activity score between RA and control samples was observed in the pathway of epigenetic regulation of gene expression, which was extracted and regarded as the seed pathway. Starting with this seed pathway, one maximum pathway set containing 10 dysregulated pathways was extracted from the PIN, having an AUC of 0.8249, and the result indicated that this pathway set could distinguish RA from the controls. These 10 dysregulated pathways might be potential biomarkers for RA diagnosis and treatment in the future.
类风湿关节炎（RA）中识别生物标志物的传统方法主要集中在差异表达的途径或单个途径上，然而，这些方法忽略了途径之间的相互作用。为了更好地理解RA的发病机制，我们旨在使用考虑途径间相互作用的途径相互作用网络（PIN）来识别失调的途径集。首先，从相应数据库中获取RA相关基因表达谱数据、蛋白质-蛋白质相互作用（PPI）数据和途径数据。其次，使用主成分分析方法计算每个途径的途径活性，然后利用从途径活性中收集的数据识别一个种子途径。接着基于基因表达谱、途径数据和PPI信息构建一个PIN。最后，使用支持向量机方法和曲线下面积（AUC）指数从基于种子途径的PIN中提取失调途径。该PIN总共包含854条途径和1064个途径相互作用。在基因表达的表观遗传调控途径中观察到RA与对照样本之间活性评分的最大变化，该途径被提取并视为种子途径。从这个种子途径开始，从PIN中提取了一个包含10条失调途径的最大途径集，其AUC为0.8249，结果表明该途径集可以区分RA与对照。这10条失调途径可能是未来RA诊断和治疗的潜在生物标志物。
