Wang Qian, Lou Zhifeng, Zhai Liansuo, Zhao Haibin
Department of Pediatrics, Jiyang Public Hospital, Jinan, Shandong Province, China.
Department of Orthopedics, Jiyang Public Hospital, Jinan, Shandong Province, China.
Indian J Pediatr. 2017 Jun;84(6):430-436. doi: 10.1007/s12098-017-2314-4. Epub 2017 Mar 1.
To identify significant biomarkers for detection of pneumococcal meningitis based on ego network.
Based on the gene expression data of pneumococcal meningitis and global protein-protein interactions (PPIs) data recruited from open access databases, the authors constructed a differential co-expression network (DCN) to identify pneumococcal meningitis biomarkers in a network view. Here EgoNet algorithm was employed to screen the significant ego networks that could accurately distinguish pneumococcal meningitis from healthy controls, by sequentially seeking ego genes, searching candidate ego networks, refinement of candidate ego networks and significance analysis to identify ego networks. Finally, the functional inference of the ego networks was performed to identify significant pathways for pneumococcal meningitis.
By differential co-expression analysis, the authors constructed the DCN that covered 1809 genes and 3689 interactions. From the DCN, a total of 90 ego genes were identified. Starting from these ego genes, three significant ego networks (Module 19, Module 70 and Module 71) that could predict clinical outcomes for pneumococcal meningitis were identified by EgoNet algorithm, and the corresponding ego genes were GMNN, MAD2L1 and TPX2, respectively. Pathway analysis showed that these three ego networks were related to CDT1 association with the CDC6:ORC:origin complex, inactivation of APC/C via direct inhibition of the APC/C complex pathway, and DNA strand elongation, respectively.
The authors successfully screened three significant ego modules which could accurately predict the clinical outcomes for pneumococcal meningitis and might play important roles in host response to pathogen infection in pneumococcal meningitis.
基于自我网络识别用于检测肺炎球菌性脑膜炎的重要生物标志物。
基于从开放获取数据库中收集的肺炎球菌性脑膜炎基因表达数据和全球蛋白质-蛋白质相互作用（PPI）数据，作者构建了一个差异共表达网络（DCN），从网络视角识别肺炎球菌性脑膜炎生物标志物。这里采用自我网络算法，通过依次寻找自我基因、搜索候选自我网络、优化候选自我网络以及显著性分析来识别自我网络，从而筛选出能够准确区分肺炎球菌性脑膜炎与健康对照的重要自我网络。最后，对自我网络进行功能推断，以识别肺炎球菌性脑膜炎的重要通路。
通过差异共表达分析，作者构建了包含1809个基因和3689个相互作用的DCN。从该DCN中，共识别出90个自我基因。从这些自我基因出发，通过自我网络算法识别出三个能够预测肺炎球菌性脑膜炎临床结局的重要自我网络（模块19、模块70和模块71），其对应的自我基因分别为GMNN、MAD2L1和TPX2。通路分析表明，这三个自我网络分别与CDT1与CDC6:ORC:起源复合物的关联、通过直接抑制APC/C复合物途径使APC/C失活以及DNA链延伸有关。
作者成功筛选出三个重要的自我模块，它们能够准确预测肺炎球菌性脑膜炎的临床结局，并且可能在肺炎球菌性脑膜炎宿主对病原体感染的反应中发挥重要作用。
