特发性肺纤维化相关基因的筛选和生物信息学分析

Screening and Bioinformatics Analysis of Idiopathic Pulmonary Fibrosis Related Genes

【目的】通过生物信息学的方法发现特发性肺纤维化(IPF)的致病基因并为进一步研究提供靶点。【方法】从GEO数据库中下载基因芯片数据集GSE53845、GSE24206、GSE10667,并使用GEO2R分析工具筛选出正常组织与IPF的差异表达基因。在DAVID数据库中对差异表达基因进行GO分析和KEGG通路富集分析,以便找到IPF发病过程中差异表达基因主要参与的生物功能及其集中的信号通路。为了研究差异表达基因与蛋白之间的作用关系,使用STRING和CYTOSCAPE软件来构建蛋白相互作用网络,使用MCODE软件来提取蛋白相互作用网络中的子网络模块。【结果】发现了110个差异表达基因,其中有92个在IPF中高表达,18个低表达。GO富集分析表明IPF中上调的差异表达基因主要影响细胞粘附、生物粘附、胶原蛋白代谢等相关的生物过程,富集的分子功能主要参与细胞外基质结构的构成、钙离子的结合;IPF中下调的蛋白则主要涉及感觉调节的生物过程。KEGG通路分析表明IPF中上调的差异表达基因主要参与受体相互作用、细胞粘附等信号通路。【结论】利用生物信息学筛选出差异表达基因,其中部分基因已被证实参与IPF,部分基因尚未有研究,提示其可能是IPF发病机制研究新的研究靶点。

【Objective】We aimed to explore the pathogenic genes of Idiopathic pulmonary fibrosis(IPF) by bioinformatics analysis and provide a target for further research.【Methods】Gene data sets GSE53845, GSE24206, GSE10667 were downloaded from the Gene Expression Omnibus database and the differential expression genes of normal tissue and IPF were screened with GEO2 R analysis tool. GO analysis and KEGG pathway enrichment analysis of differentially expressed genes were performed in DAVID database in order to find out the biological function and its focused signal pathway in differentially expressed genes during IPF development. In order to study the relationship between differential genes and proteins, STRING and CYTOSCAPE software were used to construct the protein interaction network and MCODE software was used to extract the sub-network modules in the protein-interacting network.【Results】This study found 110 differentially expressed genes, of which 92 were high expression in IPF and 18 were low expression. GO enrichment analysis showed that the up-regulated genes in IPF mainly affected the biological processes such as cell adhesion, bio-adhesion and collagen metabolism. The enriched molecular function was mainly involved in the composition of extracellular matrix structure and the binding of calcium ions. The down-regulated proteins are mainly involved in the sensory regulation of the biological process in IPF. KEGG pathway analysis showed that the up-regulated genes in IPF were mainly involved in receptor interactions, cell adhesion and other signaling pathways.【Conclusions】This study uses bioinformatics to screen out the differential genes, some of which have been shown to be involved in IPF, and some genes have not been studied, suggesting that it may be a new research target for IPF pathogenesis.