Suppr超能文献

通过综合生物信息学分析鉴定与 COVID-19 相关的免疫枢纽基因及潜在分子机制。

Identification of immune-related hub genes and potential molecular mechanisms involved in COVID-19 via integrated bioinformatics analysis.

作者信息

Zhu Rui, Zhao Yaping, Yin Hui, Shu Linfeng, Ma Yuhang, Tao Yingli

机构信息

School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China.

School of Pharmacy, Zhejiang Chinese Medical University, Hangzhou, 310053, China.

出版信息

Sci Rep. 2024 Dec 2;14(1):29964. doi: 10.1038/s41598-024-81803-2.

Abstract

COVID-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. 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. 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. 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. 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 framework for further exploring immune responses in COVID-19 patients, as well as for targeted therapy and vaccine development.

摘要

由严重急性呼吸综合征冠状病毒2(SARS-CoV-2)病毒引起的2019冠状病毒病(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发病机制中涉及的分子机制并确定关键的免疫相关基因(IRG)对于制定有效的预防和治疗策略至关重要。本研究采用计算工具分析生物学数据(生物信息学)以及一种基于基因变异推断因果关系的方法,即孟德尔随机化(MR),以探索IRG在COVID-19中的作用。我们从基因表达综合数据库(GEO)中可用的数据集中识别出差异表达基因(DEG),并将COVID-19患者与健康对照进行比较。IRG来自免疫数据库(ImmPort)。我们进行了功能富集分析、通路分析和免疫浸润评估,以确定所识别的IRG的生物学意义。共识别出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。通路富集分析表明,CD1C参与萜类骨架生物合成和辅助性T细胞17(Th17)细胞分化,而IL1B与B细胞受体信号传导和核因子κB(NF-κB)信号通路相关。观察到关键基因与各种免疫细胞之间存在显著相关性,表明它们在COVID-19中影响免疫细胞调节。本研究为COVID-19潜在的免疫机制提供了新见解,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分析方法为进一步探索COVID-19患者的免疫反应以及靶向治疗和疫苗开发提供了一个强大的框架。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60b4/11612211/78bc718819a5/41598_2024_81803_Fig1_HTML.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验