寡细胞哮喘的特征基因挖掘及其对哮喘治疗靶点的应用前景评估

Feature gene mining for prediction of paucigranulocytic asthma

目的 应用生物信息学方法对寡细胞型哮喘患者的基因表达谱芯片进行分析,以研究寡细胞型哮喘的病理机制,进而寻找寡细胞型哮喘的精准治疗靶标。方法 从GEO平台库获取GSE143303芯片数据集,在对数据集进行整理与初步分析后,对寡细胞型哮喘和健康对照的表达谱进行基因表达分析。利用R语言进行功能基因集富集分析,对基因进行功能聚类。利用基因集互作信息,构建与表型正负关联度最强的基因互作网络,并计算不同网络间的相关性,结合差异基因分析,筛选出关键基因和调控网络。结果 GSE143303数据集包含47例成人支气管活检样本,其中有16例寡细胞型哮喘和13例健康对照。筛选出显著意义的通路基因集115个,其中正相关的通路基因集有37个,负相关的通路基因集有78个。与寡细胞型哮喘强负相关的通路基因网络之间,存在基因纠缠。对互作网络进行基因共集分析,结果发现网络交叉主要出现在HLA相关基因。结合差异基因分析,发现网络交联基因TNFRSF13B、HLA-DQB1、HLA-DRB5在正常对照组相比存在表达差异,其中TNFRSF13B有显著差异。结论 TNFRSF13B、HLA-DQB1、HLA-DRB5及由此组成的基因调控网络可能是寡细胞型哮喘的关键基因与主要相关调控网络,研究结果为寻找寡细胞型哮喘的精准治疗靶标和病理机制提供了研究方向。

Objective To explore the mechanism of paucigranulocytic asthma and to find therapeutic target for paucigranulocytic asthma.Methods GSE143303 data and platform information were downloaded from GEO.Gene Set Enrichment Analysis were performed to construct positive and negative gene-gene interaction network correlation with paucigranulocytic asthma.Differential expression analysis,pathway commonality analysis were performed with R language.Results GSE143303 data set contained 47 endobronchial biopsies from adult(16 cases of paucigranulocytic asthma,13 cases of healthy control).Compared with control group,the paucigranulocytic asthma group had 115 differential genes set(37 positive and 78 negative).The results of pathway commonality analysis showed that the crosslink existed within the negative gene-gene interaction network correlation with paucigranulocytic asthma.Among these,most of the genes belonged to the protein HLA gene family.Differential expression analysis show that HLA-DQB1,HLA-DRB5 were differential genes and TNFRSF13B was significantly downregulated genes in the intersect genes.Conclusion TNFRSF13B,HLA-DQB1,HLA-DRB5 and regulatory networks associated with them are the crucial factors contributing to paucigranulocytic asthma.