Screening and Bioinformatics Analysis of Idiopathic Pulmonary Fibrosis Related Genes
- VernacularTitle:特发性肺纤维化相关基因的筛选和生物信息学分析
- Author:
Jing XING
1
;
yan Xin HUANG
;
biao Yu GUO
Author Information
1. 中山大学附属第一医院呼吸内科
- Keywords:
idiopathic pulmonary fibrosis;
differentially expressed gene;
bioinformatics analysis
- From:
Journal of Sun Yat-sen University(Medical Sciences)
2017;38(6):926-930,937
- CountryChina
- Language:Chinese
-
Abstract:
[Objective]We aimed to explore the pathogenic genes of Idiopathic pulmonary fibrosis (IPF) by bioinformatics analy?sis 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 GEO 2R analysis tool. GO analysis and KEGG pathway enrichment analysis of differentially expressed genes were performed in DAVID database in or?der 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 pro?tein interaction network and MCODE software was used to extract the sub-network modules in the protein-interacting network.[Re?sults]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-ad?hesion and collagen metabolism. The enriched molecular function was mainly involved in the composition of extracellular matrix struc?ture and the binding of calcium ions. The down-regulated proteins are mainly involved in the sensory regulation of the biological pro?cess in IPF. KEGG pathway analysis showed that the up-regulated genes in IPF were mainly involved in receptor interactions, cell ad?hesion 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.
