Benson M, Steenhoff Hov D A, Clancy T, Hovig E, Rudemo M, Cardell L O
Pediatric Allergy Research Group, Queen Silvia Children's Hospital, Goteborg, Sweden.
Acta Otolaryngol. 2007 Oct;127(10):1074-9. doi: 10.1080/00016480701200277.
The presented analysis of nasal polyposis using connectivity based on the PubGene literature co-citation network demonstrates that this tool can be used to identify key genes in DNA microarray studies of human polygenic diseases.
DNA microarray studies of complex diseases may reveal differential expression of hundreds of genes. According to network theory and studies of yeast cells, genes that are connected with several other genes appear to have key regulatory roles. This study aimed to examine if this principle can be translated to DNA microarray studies of human disease, using nasal polyposis as a base for the analysis.
The connectivity of differentially expressed genes from a previously described microarray study of nasal polyposis before and after treatment with glucocorticoids was determined. This was done using the literature co-citation network PubGene.
In all, 166 genes were differentially expressed; 39 of these were previously defined as inflammatory and considered important for nasal polyposis. The connectivity of all differentially expressed genes was analysed using the PubGene literature co-citation network. Seventy-four of the 166 genes were connected to other genes. By contrast, the average number of connected genes among 100 sets of 166 randomly chosen genes was 31.5. A small number of the differentially expressed genes were highly connected, while most genes had few or no connections. This indicated a scale-free network. The most connected gene was interleukin-8, an inflammatory gene of known importance for nasal polyposis. Twenty-eight of the 74 connected genes were inflammatory (38%), compared with 11 of the 92 unconnected genes (12%), p < 0.0001. Since most evidence suggests that nasal polyps are inflammatory in their nature, this supports the hypothesis that connected genes have more disease relevance than unconnected genes.
利用基于PubGene文献共引网络的连通性对鼻息肉进行的分析表明，该工具可用于在人类多基因疾病的DNA微阵列研究中识别关键基因。
复杂疾病的DNA微阵列研究可能揭示数百个基因的差异表达。根据网络理论和对酵母细胞的研究，与其他几个基因相连的基因似乎具有关键的调控作用。本研究旨在探讨这一原理是否可转化为人类疾病的DNA微阵列研究，以鼻息肉为分析基础。
确定了先前描述的鼻息肉糖皮质激素治疗前后微阵列研究中差异表达基因的连通性。这是使用文献共引网络PubGene完成的。
总共166个基因差异表达；其中39个先前被定义为炎症相关基因，并被认为对鼻息肉很重要。使用PubGene文献共引网络分析了所有差异表达基因的连通性。166个基因中有74个与其他基因相连。相比之下，100组随机选择的166个基因中，平均相连基因数为31.5。少数差异表达基因高度连通，而大多数基因很少或没有连接。这表明是一个无标度网络。连通性最高的基因是白细胞介素-8，这是一个对鼻息肉已知重要的炎症基因。74个相连基因中有28个是炎症相关基因（38%），而92个未相连基因中有11个是炎症相关基因（12%），p<0.0001。由于大多数证据表明鼻息肉本质上是炎症性的，这支持了相连基因比未相连基因具有更多疾病相关性的假设。
