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Zhisheng Huang,1,2 Zuowang Cheng,3 Xia Deng,4 Ying Yang,5 Na Sun,5 Peibin Hou,5 Ruyue Fan,5 Shuai Liu6,7 1Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China; 2Department of Pulmonary and Critical Care Medicine, National Regional Center for Respiratory Medicine, Jiangxi Hospital of China-Japan Friendship Hospital, Nanchang, Jiangxi, People’s Republic of China; 3Department of Clinical Laboratory, Zhangqiu District People’s Hospital Affiliated to Jining Medical University, Jinan, Shandong, People’s Republic of China; 4School of Public Health, Shandong Second Medical University, Weifang, Shandong, People’s Republic of China; 5Shandong Center for Disease Control and Prevention, Jinan, Shandong, People’s Republic of China; 6Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People’s Republic of China; 7Shandong Key Laboratory of Infectious Respiratory Disease, Jinan, Shandong, People’s Republic of ChinaCorrespondence: Shuai Liu; Ruyue Fan, Email liushuai_sam@163.com; fanruyue2022@163.comBackground: Coronavirus disease 2019 (COVID-19) is a respiratory infectious illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The objective of this study is to identify reliable and accurate biomarkers for the early stratification of disease severity, a crucial aspect that is currently lacking for the impending phases of the next COVID-19 pandemic.Methods: In this study, we identified important module and hub genes related to clinical severe COVID-19 using differentially expressed genes (DEGs) screening combing weighted gene co-expression network analysis (WGCNA) in dataset GSE213313. We further screened and confirmed these hub genes in another two new independent datasets (GSE172114 and GSE157103). In order to evaluate these key genes’ stability and robustness for diagnosing or predicting the progression of illness, we used RT-PCR validation of selected genes in blood samples obtained from hospitalized COVID-19 patients.Results: A total of 968 and 52 DEGs were identified between COVID-19 patients and normal people, critical and non-critical patients, respectively. Then, using WGCNA, 10 modules were constructed. Among them, the blue module positively associated with clinic disease severity of COVID-19. From overlapped section between DEGs and blue module, 12 intersected common differential genes were obtained. Subsequently, these hub genes were validated in another two new independent datasets as well and 9 genes that overlapped showed a highly correlation with disease severity. Finally, the mRNA expression levels of these hub genes were tested in blood samples from COVID-19 patients. In severe cases, there was increased expression of MCEMP1, ANXA3, CD177, and SCN9A. In particular, MCEMP1 increased with disease severity, which suggested an unfavorable development and a frustrating prognosis.Conclusion: Using comprehensive bioinformatical analysis and the validation of clinical samples, we identified four major candidate genes, MCEMP1, ANXA3, CD177, and SCN9A, which are essential for diagnosis or development of COVID-19.Keywords: COVID-19, differentially expressed genes, WGCNA, hub genes, neutrophil |
