Landscape of infiltrated immune cell characterization in COVID-19
Heliyon(2024)
摘要
Purpose
Although the role of SARS-CoV-2-specfic immune cells has been revealed, a comprehensive understanding of immune patterns remains unknown.
Methods
In this work, unsupervised consensus clustering analysis was used to classify 240 coronavirus disease 2019 (COVID-19) patients into different immune subtypes. Next, we performed differentially expressed analysis between different immune subtypes. Functional enrichment and pathway analyses were employed to reveal the biological significance of these differentially expressed genes (DEGs). Besides, we compared feature score of some DEGs between whole blood and lung tissues. Then, we utilized the “GSVA” algorithm to construct an immune cell infiltrating (ICI) tool based on the categories of these DEGs. Finally, we developed a nomogram associated with severity of COVID-19.
Results
As a result, we identified two immune subtypes, and 238 DEGs which mainly participated in some immune-related functions and the COVID-19 pathway. Most importantly, the 238 DEGs could reflect the characterization of immune patterns in lung tissues. ICI scores were markedly negative associated with immune scores. It was worth noting that ICI score was a strong indicator for severity of COVID-19 and could accurately predict the severity of COVID-19.
Conclusion
Our findings could provide more valuable strategies for the management of COVID-19.
更多查看译文
关键词
Coronavirus disease 2019,Immune infiltrating cells,Immune response,Gene expression,Viral infection,Severity
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要