Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method

Hao Li; Feiming Huang; Huiping Liao; Zhandong Li; Kaiyan Feng; Tao Huang; Yu-Dong Cai

doi:10.3389/fmolb.2022.952626

Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method

Front Mol Biosci. 2022 Jul 19:9:952626. doi: 10.3389/fmolb.2022.952626. eCollection 2022.

Authors

Hao Li¹, Feiming Huang², Huiping Liao³, Zhandong Li¹, Kaiyan Feng⁴, Tao Huang^{5

6}, Yu-Dong Cai²

Affiliations

¹ College of Biological and Food Engineering, Jilin Engineering Normal University, Changchun, China.
² School of Life Sciences, Shanghai University, Shanghai, China.
³ Ophthalmology and Optometry Medical School, Shandong University of Traditional Chinese Medicine, Jinan, China.
⁴ Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou, China.
⁵ Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
⁶ CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.

Abstract

Notably, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a tight relationship with the immune system. Human resistance to COVID-19 infection comprises two stages. The first stage is immune defense, while the second stage is extensive inflammation. This process is further divided into innate and adaptive immunity during the immune defense phase. These two stages involve various immune cells, including CD4⁺ T cells, CD8⁺ T cells, monocytes, dendritic cells, B cells, and natural killer cells. Various immune cells are involved and make up the complex and unique immune system response to COVID-19, providing characteristics that set it apart from other respiratory infectious diseases. In the present study, we identified cell markers for differentiating COVID-19 from common inflammatory responses, non-COVID-19 severe respiratory diseases, and healthy populations based on single-cell profiling of the gene expression of six immune cell types by using Boruta and mRMR feature selection methods. Some features such as IFI44L in B cells, S100A8 in monocytes, and NCR2 in natural killer cells are involved in the innate immune response of COVID-19. Other features such as ZFP36L2 in CD4⁺ T cells can regulate the inflammatory process of COVID-19. Subsequently, the IFS method was used to determine the best feature subsets and classifiers in the six immune cell types for two classification algorithms. Furthermore, we established the quantitative rules used to distinguish the disease status. The results of this study can provide theoretical support for a more in-depth investigation of COVID-19 pathogenesis and intervention strategies.

Keywords: COVID-19; classification algorithm; feature selection; immune cell; machine learning.