Background

Sepsis is a life-threatening condition of organ dysfunction caused by a dysregulated host immune response to infection. We performed network analysis of cytokine molecules and compared network structures between a systematic inflammatory response syndrome (SIRS) or normal control (NC) group and a sepsis group.

Results

We recruited SIRS (n = 33) and sepsis (n = 89) patients from electronic medical records (EMR) according to whether data on PCT, CRP, interleukin (IL)-1β, IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12p70, IL-13, IL-17, IL-22, TNF-α, and IFN-γ levels were available. From the public GEO dataset, GSE66099, GSE9960, GSE95233, GSE57065 were downloaded. Genes corresponding to 15 molecules were extracted from an expression array. A correlation matrix was formed for the 15 molecules and statistically significant molecular pairs were used as pairs for network analysis of coexpression. The number of molecular or gene expression pairs significantly correlated among the SIRS or control and sepsis groups are as follows for datasets: EMR, 15 and 15; GEO66099-1, 13 and 15; GEO9960, 13 and 11; GSE95233, 13 and 8; GSE66099-2, 15 and 14; GSE57065, 14 and 13, respectively. Network analysis revealed that network diameter, number of nodes and shortest path were equal to or lower in the sepsis group.

Conclusions

The coexpression network in sepsis patients was relatively small sized and had lower shortest paths compared with the SIRS group or healthy control group. Cytokines with one degree (k = 1) are increased in sepsis group compared with SIRS or healthy control group. IL-9 and IL-2 were not included in network of sepsis group indicating that these cytokines showed no correlation with other cytokines. These data might imply that cytokines tend to be dysregulated in the sepsis group compared to that of SIRS or normal control groups

中文翻译：

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败血症病例中的细胞因子和分子网络：网络生物学方法。

背景

败血症是由宿主对感染的免疫反应失调引起的威胁器官功能的致命疾病。我们进行了细胞因子分子的网络分析，比较了系统性炎症反应综合征（SIRS）或正常对照组（NC）组与败血症组之间的网络结构。

结果

根据PCT，CRP，白介素（IL）-1β，IL-2，IL-4，IL-5的数据，我们从电子病历（EMR）中招募了SIRS（n = 33）和败血症（n = 89）患者IL-6，IL-9，IL-10，IL-12p70，IL-13，IL-17，IL-22，TNF-α和IFN-γ水平可用。从公共GEO数据集下载了GSE66099，GSE9960，GSE95233，GSE57065。从表达阵列提取对应于15个分子的基因。为15个分子形成了一个相关矩阵，并且将具有统计学意义的分子对用作共表达网络分析的对。对于数据集，SIRS或对照组和败血症组之间显着相关的分子或基因表达对的数量如下：EMR，15和15；GEO66099-1、13和15；GEO9960、13和11；GSE95233、13和8；GSE66099-2、15和14；GSE57065、14和13 分别。网络分析表明败血症组的网络直径，结节数和最短路径等于或小于脓毒症组。

结论

与SIRS组或健康对照组相比，脓毒症患者的共表达网络相对较小，最短路径较短。与SIRS或健康对照组相比，脓毒症组中一度细胞因子（k = 1）增加。脓毒症组的网络中未包括IL-9和IL-2，表明这些细胞因子与其他细胞因子无相关性。这些数据可能暗示与SIRS或正常对照组相比，败血症组中的细胞因子倾向于失调。