Li Zhandong, Mei Zi, Ding Shijian, Chen Lei, Li Hao, Feng Kaiyan, Huang Tao, Cai Yu-Dong
College of Biological and Food Engineering, Jilin Engineering Normal University, Changchun, China.
Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China.
Front Mol Biosci. 2022 May 10;9:908080. doi: 10.3389/fmolb.2022.908080. eCollection 2022.
The occurrence of coronavirus disease 2019 (COVID-19) has become a serious challenge to global public health. Definitive and effective treatments for COVID-19 are still lacking, and targeted antiviral drugs are not available. In addition, viruses can regulate host innate immunity and antiviral processes through the epigenome to promote viral self-replication and disease progression. In this study, we first analyzed the methylation dataset of COVID-19 using the Monte Carlo feature selection method to obtain a feature list. This feature list was subjected to the incremental feature selection method combined with a decision tree algorithm to extract key biomarkers, build effective classification models and classification rules that can remarkably distinguish patients with or without COVID-19. EPSTI1, NACAP1, SHROOM3, C19ORF35, and MX1 as the essential features play important roles in the infection and immune response to novel coronavirus. The six significant rules extracted from the optimal classifier quantitatively explained the expression pattern of COVID-19. Therefore, these findings validated that our method can distinguish COVID-19 at the methylation level and provide guidance for the diagnosis and treatment of COVID-19.
2019冠状病毒病（COVID-19）的出现已成为全球公共卫生面临的严峻挑战。目前仍缺乏针对COVID-19的确切有效治疗方法，且尚无靶向抗病毒药物。此外，病毒可通过表观基因组调节宿主固有免疫和抗病毒过程，以促进病毒自身复制和疾病进展。在本研究中，我们首先使用蒙特卡洛特征选择方法分析COVID-19的甲基化数据集，以获得一个特征列表。该特征列表采用增量特征选择方法结合决策树算法，以提取关键生物标志物，构建能够显著区分COVID-19患者和非COVID-19患者的有效分类模型和分类规则。EPSTI1、NACAP1、SHROOM3、C19ORF35和MX1作为关键特征，在新型冠状病毒的感染和免疫反应中发挥重要作用。从最优分类器中提取的六条重要规则定量解释了COVID-19的表达模式。因此，这些发现证实我们的方法能够在甲基化水平上区分COVID-19，并为COVID-19的诊断和治疗提供指导。
