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

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.

Immune responses of different COVID-19 vaccination strategies by analyzing single-cell RNA sequencing data from multiple tissues using machine learning methods

“…These algorithms included LASSO (Ranstam and Cook, 2018), LightGBM (Ke et al, 2017), MCFS (Dramiński and Koronacki, 2018), mRMR (Peng et al, 2005), and PFI (Fisher et al, 2019). These methods have been widely practiced in solving life science problems (Zhao et al, 2018;Li et al, 2022a;Li et al, 2022b;Li Z. et al, 2022;Lu et al, 2022;Huang et al, 2023a;Huang et al, 2023b).…”

Identification of Gene Markers Associated with COVID-19 Severity and Recovery in Different Immune Cell Subtypes

“…Thus, in this study, four feature-ranking algorithms, including last absolute shrinkage and selection operator (LASSO) [13], light gradient boosting machine (LightGBM) [14], Monte Carlo feature selection (MCFS) [15], and mRMR [16], were adopted to process the single-cell data on each cell type. These methods have been recognized in the life sciences [17][18][19][20][21][22][23][24]. As they are designed following different principles, the simultaneous use of them can provide more chances to extract essential gene features.…”

“…In a clinical study in pregnant women, researchers found an increased proportion of CD8 + T cells and a high expression of MT2A in the cells of pregnant women positive for HBsAg and HBeAg [50]. With the use of a machine-learning-based approach to explore the characteristic immunomarkers of COVID-19, researchers found that CD8 + T cells in COVID-19 patients have specific MT2A up-regulation possibly by regulating intracellular metal homeostasis to influence the response of T cells to the SARS-CoV-2 infection [18].…”

Biological effects of COVID-19 on lung cancer: Can we drive our decisions

“…One of the most important aspects related to COVID-19 is the cytokine storm, during which various inflammatory cytokines are developed, and the consequent cytokine release syndrome (CRS), which represents an acute attack of excessive cytokines from the immune system triggered by inflammatory responses and the immunity ( 96 , 97 ). The CRS is characterized by high fever, erythema, edema, extreme fatigue, and nausea, which may also be associated with sepsis and multiple organ failure ( 98 ).…”