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基于生物信息学和二代测序数据分析的特发性肺纤维化潜在差异表达基因研究

Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis.

作者信息

Giriyappagoudar Muttanagouda, Vastrad Basavaraj, Horakeri Rajeshwari, Vastrad Chanabasayya

机构信息

Department of Radiation Oncology, Karnataka Institute of Medical Sciences (KIMS), Hubballi 580022, Karnataka, India.

Department of Pharmaceutical Chemistry, K.L.E. Socitey's College of Pharmacy, Gadag 582101, Karnataka, India.

出版信息

Biomedicines. 2023 Nov 21;11(12):3109. doi: 10.3390/biomedicines11123109.

Abstract

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next-generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analyses of DEGs were performed. Then, a protein-protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst databaseswereused to construct the targeted microRNAs (miRNAs), transcription factors (TFs), and small drug molecules. Finally, receiver operating characteristic (ROC) curve analysis was used to validate the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process, and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 were selected. Cyclothiazide and rotigotinethe are predicted small drug molecules for IPF treatment. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

摘要

特发性肺纤维化(IPF)是一种慢性进行性肺部疾病,会降低生活质量并导致过早死亡,但其发病机制和关键基因仍不清楚。在本研究中,运用生物信息学深入分析IPF的发病机制和相关关键基因,以探究IPF潜在的分子发病机制,并为临床治疗提供指导。从基因表达综合数据库(GEO)获取了下一代测序数据集GSE213001,鉴定出IPF组与正常对照组之间的差异表达基因(DEGs)。使用R语言的DESeq2软件包筛选IPF组与正常对照组之间的DEGs。对DEGs进行基因本体论(GO)和REACTOME通路富集分析。利用g:Profiler对DEGs进行功能和通路富集分析。然后,通过综合相互作用数据库(IID)构建蛋白质-蛋白质相互作用(PPI)网络。使用带有网络分析器的Cytoscape软件识别枢纽基因。利用miRNet和NetworkAnalyst数据库构建靶向微小RNA(miRNAs)、转录因子(TFs)和小分子药物的网络。最后,采用受试者工作特征(ROC)曲线分析验证枢纽基因。本研究共筛选出958个DEGs,其中上调基因479个,下调基因479个。大多数DEGs在对刺激的反应、G蛋白偶联受体(GPCR)配体结合、基于微管的过程以及GALNT3缺陷导致高频听力损失(HFTC)等方面显著富集。结合PPI网络、miRNA-枢纽基因调控网络和TF-枢纽基因调控网络的结果,选择了包括亮氨酸重复激酶2(LRRK2)、BMI1、鲨烯环氧酶(EBP)、髓系细胞核分化抗原(MNDA)、含BTB结构域蛋白7(KBTBD7)、角蛋白15(KRT15)、同源盒蛋白OTX1(OTX1)、微管蛋白4(TEKT4)、精子相关抗原8(SPAG8)和EF手型钙结合蛋白2(EFHC2)在内的枢纽基因。环噻嗪和罗替戈汀被预测为治疗IPF的小分子药物。我们的研究结果将有助于识别IPF潜在的生物标志物和新的治疗策略,并为临床治疗提供新的策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3da6/10740779/dc694bac5023/biomedicines-11-03109-g001.jpg

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