Journal of Multidisciplinary Healthcare (Oct 2022)
The m7G Modification Level and Immune Infiltration Characteristics in Patients with COVID-19
Abstract
Lingling Lu,1,* Jiaolong Zheng,1,2,* Bang Liu,1,* Haicong Wu,1,2 Jiaofeng Huang,1 Liqing Wu,2 Dongliang Li1,2 1Fuzong Clinical Medical College of Fujian Medical University, The 900th Hospital, Fuzhou, People’s Republic of China; 2Department of Hepatobiliary Disease, The 900th Hospital of Joint Logistics Support Force, Fuzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Dongliang Li, Fuzong Clinical Medical College of Fujian Medical University, The 900th Hospital of the People’s Liberation Army Joint Logistics Support Force, No. 156 Xierhuan Road, Fuzhou, Fujian, 350025, People’s Republic of China, Tel/Fax +86 591 22859128, Email [email protected]: The 7-methylguanosine (m7G)-related genes were used to identify the clinical severity and prognosis of patients with coronavirus disease 2019 (COVID-19) and to identify possible therapeutic targets.Patients and Methods: The GSE157103 dataset provides the transcriptional spectrum and clinical information required to analyze the expression of m7G-related genes and the disease subtypes. R language was applied for immune infiltration analysis, functional enrichment analysis, and nomogram model construction.Results: Most m7G-related genes were up-regulated in COVID-19 and were closely related to immune cell infiltration. Disease subtypes were grouped using a clustering algorithm. It was found that the m7G-cluster B was associated with higher immune infiltration, lower mechanical ventilation, lower intensive care unit (ICU) status, higher ventilator-free days, and lower m7G scores. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that differentially expressed genes (DEGs) between m7G-cluster A and B were enriched in viral infection and immune-related aspects, including COVID-19 infection; Th17, Th1, and Th2 cell differentiation, and human T-cell leukemia virus 1 infection. Finally, through machine learning, six disease characteristic genes, NUDT4B, IFIT5, LARP1, EIF4E, LSM1, and NUDT4, were screened and used to develop a nomogram model to estimate disease risk.Conclusion: The expression of most m7G genes was higher in COVID-19 patients compared with that in non-COVID-19 patients. The m7G-cluster B showed higher immune infiltration and milder symptoms. The predictive nomogram based on the six m7G genes can be used to accurately assess risk.Keywords: COVID-19, 7-methylguanosine, SARS-CoV-2, nomogram, risk, immune cells
