本研究从美国国立生物信息中心(NCBI)的子数据库基因表达数据库(GEO)中选择基因表达谱GSE36830数据集, 采用GEO2R筛选正常钩突和鼻息肉组织之间的差异表达基因(DEGs), 对关键通路和差异表达基因进行数据库挖掘和分析, 经筛选后的差异表达基因采用戴维在线工具对其进行基因本体富集分析(GO)、京都基因和基因组百科全书(KEGG)分析, 然后将DEGs导入String数据库进行蛋白质互作网络分析, 绘制差异表达基因互作网络图, 将其数据导入Cytoscape软件中, 筛选网络中心节点和关键基因, 分析关键子网络。共筛选出699个DEGs, 其中475个基因为上调表达基因, 224个基因为下调表达基因。在GO分析中, 针对生物过程, 上调的DEGs包括: 炎症反应、免疫反应、细胞趋化性、炎症反应的正向调节和细胞的粘附等; 下调的DEGs主要参与: 唾液分泌、生物矿物组织发展、细胞氨基酸生物合成过程、视网膜内稳态及离子跨膜转运等。在KEGG分析中, 上调的DEGs主要在参与造血细胞系、细胞因子-细胞因子受体相互作用、破骨细胞分化、趋化因子信号通路、癌症中的转录失调、哮喘、金黄色葡萄球菌感染等信号通路中富集, 而下调的DEGs在唾液腺分泌及胆汁分泌信号通路中富集。差异表达基因互作网络图筛选出前10个关键基因: ITGAM、IL10、CD86、TLR8、ITGAX、CCL2、CCR7、SRC、EGF 及ITGB2。本研究得到了一组鼻息肉差异表达基因的生物信息学分析结果, 但仍需进一步用基础试验来验证。本文分析的结论为慢性鼻-鼻窦炎、鼻息肉的研究提供了新的研究方向, 也为鼻息肉发病机制研究的思路提供了一定的建设性作用。

The data-set of chronic rhinosinusitis expression profiles was downloaded from NCBI sub-database Gene Expression Omnibus. The differentially expressed genes were analyzed by GEO2R. The DEGs were analyzed and shown by the volcano plot. The GO analysis and KEGG analysis were performed by using David online tool.The DEGs were introduced into the String online data-base for analysis and the differential gene interaction network map was drawn.The interaction network data was imported into Cytoscape software, which can select Hub genes. The key sub-network was analyzed. A total of 699 DEGs were screened, of which 475 genes were up-regulated genes and 224 genes were down-regulated genes.The GO analysis showed that up-regulated DEGs were enriched in biological processes significantly,including inflammatory response, immune response,positive regulation of inflammatory response,chemotaxis and cell adhesion. The down-regulated DEGs were enriched in saliva secretion, biomineral tissue development, cellular amino acid biosynthetic process, retina homeostasis and ion transmembrane transport.The KEGG pathway analysis showed that up-regulated DEGs were enriched in hematopoietic cell lineage, cytokine-cytokine receptor interaction,osteoclast differentiation,chemokine signaling pathway. The down-regulated DEGs were enriched in salivary secretion, bile secretion.The top 10 Hub genes contained ITGAM, IL10, CD86, TLR8, ITGAX, CCL2, CCR7, SRC, EGF and ITGB2, which were identified from PPI network. The results provide a comprehensive bioinformatics analysis of DEGs in chronic rhinosinusitis with nasal polyps, however, more fundamental research is needed to validate the findings.Not only can the conclusion provide a new idea for the research direction of chronic rhinosinusitis and nasal polyps,but also make a suggestive effect on the pathogenesis of nasal polyps.