分析与系统性红斑狼疮和 COVID-19 相关的关键基因

IF 1.6 4区 医学 Q4 BIOCHEMICAL RESEARCH METHODS
Rui Guan, Jing Yu, Jiannan Zheng, Yeyu Zhao, Bolun Zhang, Min Wang, Mingli Gao
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引用次数: 0

摘要

背景:系统性红斑狼疮(SLE)是一种多因素、复杂的免疫性疾病:系统性红斑狼疮(SLE)是一种多因素、复杂的免疫性疾病;然而,COVID-19 感染与系统性红斑狼疮患者的相关性仍不确定。目的:本研究旨在探索系统性红斑狼疮患者体内的关键候选基因和通路,并试图利用生物信息学分析来揭示系统性红斑狼疮和COVID-19患者体内固有的分子特征。最终目的是确定与感染 SARS-CoV-2 的系统性红斑狼疮患者特别相关的潜在靶点和标记:方法:使用鲁棒等级聚合(RRA)方法分析了基因表达总库(GEO)数据库中的数据集(GSE12374、GSE20864、GSE61635、GSE81622 和 GSE144390),以确定系统性红斑狼疮患者与健康人相比的差异表达基因(DEGs)。此外,还进行了基因本体(GO)和京都基因组百科全书(KEGG)通路分析、组织特异性基因分析以及蛋白质-蛋白质相互作用(PPI)网络分析。最后,利用维恩图确定了COVID-19基因的交叉点,作为系统性红斑狼疮患者感染COVID-19的潜在靶点:结果:共发现了 154 个 DEGs,GO 富集表明它们主要参与了对病毒的防御反应(PConclusion:表现出差异表达的IFIT1、IFIT3、MX1、MX2和OAS3以及与COVID-19相关的通路有可能成为感染COVID-19的系统性红斑狼疮患者的生物标志物和治疗靶点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Key Genes Related to Systemic Lupus Erythematosus and COVID-19.

Background: Systemic Lupus Erythematosus (SLE) is a multifactorial and complex immune disease; however, the relevance of COVID-19 infection in SLE patients remains uncertain.

Aim: This study aims to explore the key candidate genes and pathways in patients with SLE. It also seeks to employ bioinformatics analysis to unravel the molecular signatures inherent in both SLE and COVID-19 patients. The ultimate aim is to identify potential targets and markers specifically relevant to SLE patients who contract SARS-CoV-2.

Methods: Datasets (GSE12374, GSE20864, GSE61635, GSE81622, and GSE144390) from the Gene Expression Omnibus (GEO) database were analyzed using Robust Rank Aggregation (RRA) method to identify differential expression genes (DEGs) in SLE patients compared to healthy individuals. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, tissue-specific gene analysis, and Protein-protein interaction (PPI) network were performed. Finally, the Venn diagram was employed to identify the intersections of COVID-19 genes, serving as potential targets for SLE patients with COVID-19 infection.

Results: A total of 154 DEGs were discovered, with GO enrichment indicating a predominant involvement in the defense response against the virus (P<0.001). KEGG pathway analysis showed enrichment in the NOD-like receptor signaling pathway and coronavirus disease, specifically COVID-19 (P<0.001). Tissue-specific genes related to the hematological and immune systems were emphasized (74%). The PPI network highlighted 22 genes, and 5 key genes, namely, IFIT1, IFIT3, MX1, MX2, and OAS3, which were identified after intersecting with COVID-19 patients' data.

Conclusion: IFIT1, IFIT3, MX1, MX2, and OAS3 exhibiting differential expression, as well as the pathways associated with COVID-19, could potentially function as biomarkers and therapeutic targets for individuals with SLE infected with COVID-19.

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来源期刊
CiteScore
3.10
自引率
5.60%
发文量
327
审稿时长
7.5 months
期刊介绍: Combinatorial Chemistry & High Throughput Screening (CCHTS) publishes full length original research articles and reviews/mini-reviews dealing with various topics related to chemical biology (High Throughput Screening, Combinatorial Chemistry, Chemoinformatics, Laboratory Automation and Compound management) in advancing drug discovery research. Original research articles and reviews in the following areas are of special interest to the readers of this journal: Target identification and validation Assay design, development, miniaturization and comparison High throughput/high content/in silico screening and associated technologies Label-free detection technologies and applications Stem cell technologies Biomarkers ADMET/PK/PD methodologies and screening Probe discovery and development, hit to lead optimization Combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries) Chemical library design and chemical diversity Chemo/bio-informatics, data mining Compound management Pharmacognosy Natural Products Research (Chemistry, Biology and Pharmacology of Natural Products) Natural Product Analytical Studies Bipharmaceutical studies of Natural products Drug repurposing Data management and statistical analysis Laboratory automation, robotics, microfluidics, signal detection technologies Current & Future Institutional Research Profile Technology transfer, legal and licensing issues Patents.
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