Identification of SARS-CoV-2 infection related genes based on the blood leukocytes sequencing dataset of patients with COVID-19

The coronavirus disease 2019 (COVID-19) is a global epidemic disease caused by a novel virus, SARS-CoV-2, causing serious adverse effects on human health. The development of more effective methods for the diagnosis and treatment of COVID-19 is of great significance to control the spread of the epidemic. In this study, we collected the blood leukocyte sequencing dataset of COVID-19 patients from the GEO database and obtained differentially expressed genes (DEGs). We further analyzed these DEGs by protein-protein interaction (PPI) analysis and gene ontology (GO) enrichment analysis and obtained the DEGs closely related to SARS-CoV-2 infection. Then, we constructed a 6-gene model, composed of 6 genes (IFIT3, OASL, USP18, XAF1, IFI27, and EPSTI1) by logistic regression analysis, and the area under the ROC curve (AUC) was calculated. The AUC of the training group, testing group, and entire group was 0.930, 0.914, and 0.921, respectively, and this model combined with ferritin and fibrinogen in the diagnosis of COVID-19 can achieve better diagnostic efficiency (AUC=0.976). The results of expression analysis showed that these six genes were highly expressed in patients with COVID-19 and positively correlated with the expression of SARS-CoV-2 invasion related genes (ACE2, TMPRSS2, CTSB, and CTSL). The risk score calculated by this model was also positively correlated with the expression of TMPRSS2, CTSB, and CTSL, indicating that the six genes were closely related to SARS-CoV-2 infection. In conclusion, our study comprehensively analyzed the functions of DEGs in blood leukocytes of COVID-19 patients and constructed a 6-gene model that may contribute to the diagnosis of COVID-19, which contributes to the development of new diagnostic and therapeutic ideas for COVID-19. Also, these six genes may be therapeutic targets for COVID-19.