AbstractCOVID-19, caused by the SARS-CoV-2 virus, poses significant health challenges worldwide, particularly due to severe immune-related complications. Understanding the molecular mechanisms and identifying key immune-related genes (IRGs) involved in COVID-19 pathogenesis is critical for developing effective prevention and treatment strategies.

摘要由SARS-CoV-2病毒引起的新型冠状病毒（CoVID-19）在全球范围内构成了重大的健康挑战，特别是由于严重的免疫相关并发症。了解COVID-19发病机制的分子机制并鉴定关键的免疫相关基因（IRGs）对于制定有效的预防和治疗策略至关重要。

This study employed computational tools to analyze biological data (bioinformatics) and a method for inferring causal relationships based on genetic variations, known as Mendelian randomization (MR), to explore the roles of IRGs in COVID-19. We identified differentially expressed genes (DEGs) from datasets available in the Gene Expression Omnibus (GEO), comparing COVID-19 patients with healthy controls.

这项研究使用计算工具分析生物数据（生物信息学）和基于遗传变异推断因果关系的方法，称为孟德尔随机化（MR），以探索IRG在COVID-19中的作用。我们从Gene Expression Omnibus（GEO）中可用的数据集中鉴定了差异表达基因（DEG），将COVID-19患者与健康对照进行了比较。

IRGs were sourced from the ImmPort database. We conducted functional enrichment analysis, pathway analysis, and immune infiltration assessments to determine the biological significance of the identified IRGs. A total of 360 common differential IRGs were identified. Among these genes, CD1C, IL1B, and SLP1 have emerged as key IRGs with potential protective effects against COVID-19.

IRG来自ImmPort数据库。。总共确定了360个常见的差异IRG。在这些基因中，CD1C，IL1B和SLP1已成为对COVID-19具有潜在保护作用的关键IRG。

Pathway enrichment analysis revealed that CD1C is involved in terpenoid backbone biosynthesis and Th17 cell differentiation, while IL1B is linked to B-cell receptor signaling and the NF-kappa B signaling pathway. Significant correlations were observed between key genes and various immune cells, suggesting that they influence immune cell modulation in COVID-19.

。在关键基因和各种免疫细胞之间观察到显着的相关性，表明它们影响COVID-19中的免疫细胞调节。

This study provides new insights into the immune mechanisms underlying COVID-19, highlighting the crucial role of IRGs in disease progression. These findings suggest that CD1C and IL1B could be potential therapeutic targets. The integrated bioinformatics and MR analysis approach offers a robust fr.

这项研究为新型冠状病毒肺炎的免疫机制提供了新的见解，突出了IRG在疾病进展中的关键作用。这些发现表明CD1C和IL1B可能是潜在的治疗靶点。综合生物信息学和MR分析方法提供了强大的fr。

IntroductionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to evolve and spread worldwide. As of 18th February 2024, there were 774,699,366 confirmed cases of COVID-19 globally, including 7,033,430 deaths, reported to the World Health Organization (WHO) (https://covid19.who.int/).

引言严重急性呼吸综合征冠状病毒2（SARS-CoV-2）继续在全球范围内发展和传播。(https://covid19.who.int/)。

Due to the highly contagious and variant nature of the virus, ongoing outbreaks of COVID-19 pose significant challenges to the sustainability of global public health systems and medical infrastructure. SARS-CoV-2 utilizes angiotensin-converting enzyme-2 (ACE-2) and transmembrane serine protease-2 (TMPRSS2), which are expressed on type 2 pneumocytes and many other cell types, as receptors to facilitate the fusion of its envelope with the cell membrane and penetration of the cell1,2.

由于该病毒具有高度传染性和变异性，持续爆发的新型冠状病毒对全球公共卫生系统和医疗基础设施的可持续性构成重大挑战。。

The main symptoms of COVID-19 include cough, fever, shortness of breath, and pneumonia. The clinical severity ranges from asymptomatic to mild respiratory symptoms and even critical conditions3 and may be accompanied by severe complications involving the immune system4.The immune system plays a central role in the pathogenesis of COVID-19.

新型冠状病毒肺炎的主要症状包括咳嗽、发烧、呼吸急促和肺炎。临床严重程度从无症状到轻度呼吸道症状，甚至是危重症3，并可能伴有涉及免疫系统的严重并发症4。免疫系统在COVID-19的发病机制中起着核心作用。

Increasing evidence has shown that immune-related genes are closely associated with the onset and progression of COVID-19 5, 6. An imbalanced immune response during viral invasion is an important immunopathological mechanism in severe disease7. In particular, cytokine storms and excessive activation of the immune system are closely associated with the severity of COVID-19 8.

越来越多的证据表明，免疫相关基因与COVID-19的发生和发展密切相关5,6。病毒侵袭过程中不平衡的免疫反应是严重疾病的重要免疫病理机制7。。

Research indicates that a “cytokine storm” may occur in severe COVID-19 and result in a worse prognosis9,10. A recent large-scale trial indicated that tocilizumab reduces mortality rates in some groups of hospitalized patients, consistent with previous Mendelian randomization (MR) studi.

研究表明，严重的新型冠状病毒肺炎可能会发生“细胞因子风暴”，并导致预后更差9,10。最近的一项大规模试验表明，托珠单抗可降低某些住院患者的死亡率，这与之前的孟德尔随机（MR）研究一致。

Data availability

数据可用性

The datasets generated during the current study are available from the corresponding author on reasonable request.

当前研究期间生成的数据集可根据合理要求从通讯作者处获得。

AbbreviationsSARS-CoV-2:

严重急性呼吸系统综合征冠状病毒2型缩写：

Revere Acute Respiratory Syndrome Coronavirus 2

里维尔急性呼吸综合征冠状病毒2

ACE-2:

ACE-2：

Angiotensin Converting Enzyme-2

血管紧张素转换酶-2

TMPRSS2:

tmprs2:

Transmembrane Serine Protease-2

跨膜丝氨酸蛋白酶-2

MR:

先生：

Mendelian Randomization

孟德尔随机化

RCTs:

随机对照试验：

Randomized Controlled Trials

随机对照试验

GO:

转到：

Gene Ontology

基因本体论

KEGG:

桶：

Kyoto Encyclopedia of Genes and Genomes

GSEA:

GSEA：

Gene Set Enrichment Analysis

基因组富集分析

DEGs:

学位：

Differentially Expressed Genes

差异表达基因

IRGs:

IRG：

Immune-related Genes

免疫相关基因

GWAS:

GWAS：

Genome-wide Association Studies

全基因组关联研究

TFs:

TFs：

Transcription Factors

转录因子

GEO:

地理位置：

Gene Expression Omnibus

基因表达综合

SNPs:

单核苷酸多态性：

Single Nucleotide Polymorphisms

单核苷酸多态性

IVs:

IV：

Instrumental Variables

工具变量

eQTL:

eQTL：

Expression Quantitative Trait Loci

表达数量性状基因座

ssGSEA:

ssGSEA：

Single-sample Gene-set Enrichment Analysis

单样本基因组富集分析

NES:

NES：

Normalized Enrichment Score

归一化富集分数

AUC:

AUC：

Area Under the Curve

曲线下面积

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Acknowledgements

致谢

We are deeply grateful for the support from the Institute of Biotechnology, Academy of Military Medical Sciences, in providing essential COVID-19-related background information. We thank all the participants and consortiums for their contributions to the original GWASs.FundingThis work was supported by the Science and Technology Project of Traditional Chinese Medicine of Zhejiang Province (2023ZL729, 2023ZL328) and the Scientific Research Fund of Zhejiang Provincial Education Department (202251034).Author informationAuthor notesRui Zhu and Yaping Zhao contributed equally to this work.Authors and AffiliationsSchool of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, ChinaRui ZhuDepartment of Reproductive Immunology, Tongde Hospital of Zhejiang Province, Hangzhou, 310012, ChinaYingli TaoDepartment of Pharmacy, Shaoxing Hospital of Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medical University, Shaoxing, 312000, ChinaYaping ZhaoSchool of Pharmacy, Zhejiang Chinese Medical University, Hangzhou, 310053, ChinaRui ZhuAnimal Science and Technology College, Beijing University of Agriculture, Beijing, 102206, ChinaHui YinSchool of Traditional Chinese Medicine, Shenyang Pharmaceutical University, Shenyang, 110016, ChinaLinfeng Shu & Yuhang MaAuthorsRui ZhuView author publicationsYou can also search for this author in.

我们非常感谢军事医学科学院生物技术研究所提供的重要COVID-19相关背景信息的支持。我们感谢所有参与者和财团对原始GWAS的贡献。资助这项工作得到了浙江省中医药科技项目（2023ZL7292023ZL328）和浙江省教育厅科研基金（202251034）的支持。作者信息作者注朱瑞丽和赵亚萍对这项工作做出了同样的贡献。作者和所属南京中医药大学药学院，南京，210023，朱瑞瑞浙江省同德医院生殖免疫学系，杭州，310012，陶英利浙江中医药大学附属绍兴中医院药学系，绍兴，312000，浙江中医药大学赵亚平药学院，杭州，310053，北京农业大学中国瑞转医学院，北京，102206，沈阳药科大学中国惠阴中医学院，沈阳，110016，中国林峰舒和余杭MaAuthorsRui ZhuView作者出版物您也可以在中搜索此作者。

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PubMed Google ScholarContributionsYingli Tao and Rui Zhu were responsible for designing and supervising the study. Yaping Zhao and Hui Yin extracted raw GWAS summary data from public databases. Linfeng Shu and Yuhang Ma participated in the data analysis and interpretation. Rui Zhu, Yaping Zhao, and Hui Yin contributed to the writing and revision of the manuscript.Corresponding authorCorrespondence to.

PubMed谷歌学术贡献陶英利和朱睿负责设计和监督这项研究。赵亚平和尹辉从公共数据库中提取了原始GWAS摘要数据。舒林峰和马余杭参与了数据分析和解释。朱睿，赵亚平和尹辉为手稿的撰写和修订做出了贡献。。

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Reprints and permissionsAbout this articleCite this articleZhu, R., Zhao, Y., Yin, H. et al. Identification of immune-related hub genes and potential molecular mechanisms involved in COVID-19 via integrated bioinformatics analysis.

转载和许可本文引用本文Zhu，R.，Zhao，Y.，Yin，H。等人。通过综合生物信息学分析鉴定COVID-19中涉及的免疫相关中枢基因和潜在分子机制。

Sci Rep 14, 29964 (2024). https://doi.org/10.1038/s41598-024-81803-2Download citationReceived: 08 July 2024Accepted: 29 November 2024Published: 02 December 2024DOI: https://doi.org/10.1038/s41598-024-81803-2Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

Sci Rep 1429964（2024）。https://doi.org/10.1038/s41598-024-81803-2Download引文接收日期：2024年7月8日接受日期：2024年11月29日发布日期：2024年12月2日OI：https://doi.org/10.1038/s41598-024-81803-2Share本文与您共享以下链接的任何人都可以阅读此内容：获取可共享链接对不起，本文目前没有可共享的链接。复制到剪贴板。

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KeywordsCOVID-19Immune-related genesBioinformaticsMendelian randomizationImmune infiltrationPathway analysis

关键词Vid-19免疫相关基因信息孟德尔随机化免疫浸润途径分析