Detection of the DNA methylation of seven genes contributes to monitoring recovery from COVID-19
DOI:
https://doi.org/10.3855/jidc.21864Keywords:
COVID-19, DNA methylation, flow cytometry, recoveryAbstract
Introduction: DNA methylation might influence the expression of genes that regulate coronavirus disease 2019 (COVID-19) progression. This work explored the significance of DNA methylation of 7 genes (TAC1, CDO1, HOXA9, ZFP42, SOX17, RASSF1A, and SHOX2) in blood circulating free DNA (cfDNA) in differentiating COVID-19 infections and recoveries. The correlation with changes in the proportion of immune cell populations in the recovery period was analyzed.
Methodology: 18 COVID-19-infected, 65 COVID-19-recovered, and 11 uninfected individuals were included. DNA methylation expression was determined by quantitative multiplex methylation-specific PCR (qMSP). The immune function of the recovered group versus uninfected group was evaluated by full-spectrum flow cytometry.
Results: The infected population showed a higher methylation positivity rate for 7 genes compared to the uninfected/recovered population. A model was constructed to distinguish the infected patients from uninfected/recovered individuals using the methylation status of 7 genes, with a sensitivity, specificity and area under curve (AUC) of 0.889, 0.842 and 0.931, respectively. The results of flow cytometry revealed that CD8+ T cells and CD38+ CD8+ T cells were significantly upregulated in recovered individuals compared to those in uninfected individuals. DNA methylation was correlated with immune cell changes, with a significant increase in the percentage of T cells, PD-1+ function CD4+ T cells, TCRγδ+ cells, and CD38+ NKT cells upon an increase in 7 gene methylation positivity.
Conclusions: This work revealed the significance of 7 gene methylation in the diagnosis of COVID-19 recovery, and demonstrated that these genes were significant in evaluating the immune function during the recovery period.
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Copyright (c) 2026 Xiao Xiao, Ren Xu, Chaoxiang Du, Jun Yin, Beibei Xin, Zhonghe Ke, Xiyan Li, Hao Zhang, Xinyu Chen

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