特发性肺纤维化和胃食管反流病遗传效应的预测和分析。

IF 1.9 4区 生物学 Q4 CELL BIOLOGY
Peipei Chen, Lubin Xie, Leikai Ma, Xianda Zhao, Yong Chen, Zhouling Ge
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引用次数: 0

摘要

随着对特发性肺纤维化(IPF)和胃食管反流病(GERD)的研究越来越多,越来越多的研究表明GERD与IPF有关,但其潜在的病理机制尚不清楚。本研究的目的是通过生物信息学分析来鉴定和分析IPF和GERD之间的差异表达基因(DEGs),并探索相关的分子机制。从GEO数据库下载四个GEO数据集(GSE24206、GSE53845、GSE26886和GSE39491),并使用在线工具GEO2R识别IPF和GERD之间的DEG。随后,进行了一系列生物信息学分析,包括京都基因和基因组百科全书(KEGG)和基因本体论(GO)富集分析、PPI网络、生物学特性、TF基因相互作用、TF miRNA协同调节网络和药物分子的预测。IPF和GERD共鉴定出71个DEG基因。五种KEGG通路,包括阿米巴病、蛋白质消化和吸收、松弛素信号通路、糖尿病并发症中的AGE-RAGE信号通路和药物代谢-细胞色素P450,都显著富集。此外,通过Cytoscape软件从PPI网络中选择了8个枢纽基因,包括POSTN、MMP1、COL3A1、COL1A2、CXCL12、TIMP3、VCAM1和COL1A1。然后,通过GEO数据集验证了对IPF和GERD具有高诊断价值的五个枢纽基因(MMP1、POSTN、COL3A1、COL1A2和COL1A1)。最后,TF基因和miRNA的相互作用与中枢基因进行了鉴定,并预测了IPF和GERD的药物分子。结果表明,西替利嗪、木犀草素和pempidine对IPF和GERD可能具有潜在的治疗价值。这项研究将为识别IPF和GERD的潜在生物标志物和有价值的治疗靶点提供新的策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Prediction and analysis of genetic effect in idiopathic pulmonary fibrosis and gastroesophageal reflux disease

Prediction and analysis of genetic effect in idiopathic pulmonary fibrosis and gastroesophageal reflux disease

With increasing research on idiopathic pulmonary fibrosis (IPF) and gastroesophageal reflux disease (GERD), more and more studies have indicated that GERD is associated with IPF, but the underlying pathological mechanisms remain unclear. The aim of the present study is to identify and analyse the differentially expressed genes (DEGs) between IPF and GERD and explore the relevant molecular mechanisms via bioinformatics analysis. Four GEO datasets (GSE24206, GSE53845, GSE26886, and GSE39491) were downloaded from the GEO database, and DEGs between IPF and GERD were identified with the online tool GEO2R. Subsequently, a series of bioinformatics analyses are conducted, including Kyoto Encyclopaedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses, the PPI network, biological characteristics, TF-gene interactions, TF-miRNA coregulatory networks, and the prediction of drug molecules. Totally, 71 genes were identified as DEGs in IPF and GERD. Five KEGG pathways, including Amoebiasis, Protein digestion and absorption, Relaxin signalling pathway, AGE-RAGE signalling pathway in diabetic complications, and Drug metabolism - cytochrome P450, were significantly enriched. In addition, eight hub genes, including POSTN, MMP1, COL3A1, COL1A2, CXCL12, TIMP3, VCAM1, and COL1A1 were selected from the PPI network by Cytoscape software. Then, five hub genes (MMP1, POSTN, COL3A1, COL1A2, and COL1A1) with high diagnostic values for IPF and GERD were validated by GEO datasets. Finally, TF-gene and miRNA interaction was identified with hub genes and predicted drug molecules for the IPF and GERD. And the results suggest that cetirizine, luteolin, and pempidine may have great potential therapeutic value in IPF and GERD. This study will provide novel strategies for the identification of potential biomarkers and valuable therapeutic targets for IPF and GERD.

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来源期刊
IET Systems Biology
IET Systems Biology 生物-数学与计算生物学
CiteScore
4.20
自引率
4.30%
发文量
17
审稿时长
>12 weeks
期刊介绍: IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches. Papers that analyse genomic data in order to identify variables and basic relationships between them are considered if the results provide a basis for mathematical modelling and simulation of cellular dynamics. Manuscripts on molecular and cell biological studies are encouraged if the aim is a systems approach to dynamic interactions within and between cells. The scope includes the following topics: Genomics, transcriptomics, proteomics, metabolomics, cells, tissue and the physiome; molecular and cellular interaction, gene, cell and protein function; networks and pathways; metabolism and cell signalling; dynamics, regulation and control; systems, signals, and information; experimental data analysis; mathematical modelling, simulation and theoretical analysis; biological modelling, simulation, prediction and control; methodologies, databases, tools and algorithms for modelling and simulation; modelling, analysis and control of biological networks; synthetic biology and bioengineering based on systems biology.
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