Meng Yu-Xiu, Liu Quan-Hong, Chen Deng-Hong, Meng Ying
Department of Neonatology, First People's Hospital of Jining, Jining, Shandong 272011, PR China.
Department of Neonatology, Maternal and Child Health Hospital of Sishui, Jining, Shandong 273200, PR China.
Comput Biol Chem. 2017 Jun;68:101-106. doi: 10.1016/j.compbiolchem.2017.02.007. Epub 2017 Feb 27.
Despite advances in neonatal care, sepsis remains a major cause of morbidity and mortality in neonates worldwide. Pathway cross-talk analysis might contribute to the inference of the driving forces in bacterial sepsis and facilitate a better understanding of underlying pathogenesis of neonatal sepsis.
This study aimed to explore the critical pathways associated with the progression of neonatal sepsis by the pathway cross-talk analysis.
By integrating neonatal transcriptome data with known pathway data and protein-protein interaction data, we systematically uncovered the disease pathway cross-talks and constructed a disease pathway cross-talk network for neonatal sepsis. Then, attract method was employed to explore the dysregulated pathways associated with neonatal sepsis. To determine the critical pathways in neonatal sepsis, rank product (RP) algorithm, centrality analysis and impact factor (IF) were introduced sequentially, which synthetically considered the differential expression of genes and pathways, pathways cross-talks and pathway parameters in the network. The dysregulated pathways with the highest IF values as well as RP<0.01 were defined as critical pathways in neonatal sepsis.
By integrating three kinds of data, only 6919 common genes were included to perform the pathway cross-talk analysis. By statistic analysis, a total of 1249 significant pathway cross-talks were selected to construct the pathway cross-talk network. Moreover, 47 dys-regulated pathways were identified via attract method, 20 pathways were identified under RP<0.01, and the top 10 pathways with the highest IF were also screened from the pathway cross-talk network. Among them, we selected 8 common pathways, i.e. critical pathways.
In this study, we systematically tracked 8 critical pathways involved in neonatal sepsis by integrating attract method and pathway cross-talk network. These pathways might be responsible for the host response in infection, and of great value for advancing diagnosis and therapy of neonatal sepsis.
尽管新生儿护理取得了进展，但败血症仍是全球新生儿发病和死亡的主要原因。通路串扰分析可能有助于推断细菌性败血症的驱动因素，并促进对新生儿败血症潜在发病机制的更好理解。
本研究旨在通过通路串扰分析探索与新生儿败血症进展相关的关键通路。
通过将新生儿转录组数据与已知通路数据和蛋白质-蛋白质相互作用数据整合，我们系统地揭示了疾病通路串扰，并构建了新生儿败血症的疾病通路串扰网络。然后，采用吸引法探索与新生儿败血症相关的失调通路。为了确定新生儿败血症中的关键通路，依次引入了秩积（RP）算法、中心性分析和影响因子（IF），综合考虑了网络中基因和通路的差异表达、通路串扰和通路参数。IF值最高且RP<0.01的失调通路被定义为新生儿败血症中的关键通路。
通过整合三种数据，仅纳入6919个共同基因进行通路串扰分析。经统计分析，共选择1249个显著的通路串扰构建通路串扰网络。此外，通过吸引法鉴定出47条失调通路，在RP<0.01的情况下鉴定出20条通路，并且还从通路串扰网络中筛选出IF最高的前10条通路。其中，我们选择了8条共同通路，即关键通路。
在本研究中，我们通过整合吸引法和通路串扰网络系统地追踪了8条参与新生儿败血症的关键通路。这些通路可能负责感染中的宿主反应，对推进新生儿败血症的诊断和治疗具有重要价值。
