Development of a 6-gene model for COVID-19 diagnosis and treatment based on the blood leukocytes sequencing dataset of patients with SARS-CoV-2 infection

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.

2019冠状病毒病（coronavirus disease 2019, COVID-19）是由新型病毒严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）引发的全球性传染病，对人类健康造成严重危害。研发更为有效的新冠诊断与治疗手段，对于遏制疫情扩散具有重要意义。本研究从基因表达综合数据库（Gene Expression Omnibus, GEO）中获取了新型冠状病毒肺炎患者的血液白细胞测序数据集，并筛选得到差异表达基因（differentially expressed genes, DEGs）。我们进一步通过蛋白质相互作用（protein-protein interaction, PPI）分析与基因本体（gene ontology, GO）富集分析对上述差异表达基因进行研究，筛选出与SARS-CoV-2感染密切相关的差异表达基因。随后，通过logistic回归分析构建了由IFIT3、OASL、USP18、XAF1、IFI27及EPSTI1这6个基因组成的诊断模型，并计算了受试者工作特征曲线（receiver operating characteristic curve, ROC）下面积（area under the ROC curve, AUC）。训练集、测试集及全数据集的AUC值分别为0.930、0.914与0.921；将该模型与铁蛋白、纤维蛋白原联合用于新冠诊断时，诊断效能更佳，AUC可达0.976。表达分析结果显示，这6个基因在新冠患者体内呈高表达状态，且与SARS-CoV-2入侵相关基因（血管紧张素转换酶2（angiotensin-converting enzyme 2, ACE2）、跨膜丝氨酸蛋白酶2（transmembrane serine protease 2, TMPRSS2）、组织蛋白酶B（cathepsin B, CTSB）及组织蛋白酶L（cathepsin L, CTSL））的表达水平呈正相关。该模型计算得到的风险评分同样与TMPRSS2、CTSB及CTSL的表达水平呈正相关，提示这6个基因与SARS-CoV-2感染密切相关。综上，本研究全面分析了新冠患者血液白细胞中的差异表达基因功能，并构建了可用于新冠诊断的6基因诊断模型，可为新冠的诊疗新思路开发提供参考。此外，这6个基因或可作为新冠的治疗靶点。