ABSTRACT
Critical patients and intensive care unit (ICU) patients are the main population of COVID-19 deaths. Therefore, establishing a reliable method is necessary for COVID-19 patients to distinguish patients who may have critical symptoms from other patients. In this retrospective study, we firstly evaluated the effects of 54 laboratory indicators on critical illness and death in 3044 COVID-19 patients from the Huoshenshan hospital in Wuhan, China. Secondly, we identify the eight most important prognostic indicators (neutrophil percentage, procalcitonin, neutrophil absolute value, C-reactive protein, albumin, interleukin-6, lymphocyte absolute value and myoglobin) by using the random forest algorithm, and find that dynamic changes of the eight prognostic indicators present significantly distinct within differently clinical severities. Thirdly, our study reveals that a model containing age and these eight prognostic indicators can accurately predict which patients may develop serious illness or death. Fourthly, our results demonstrate that different genders have different critical illness rates compared with different ages, in particular the mortality is more likely to be attributed to some key genes (e.g. ACE2, TMPRSS2 and FURIN) by combining the analysis of public lung single cells and bulk transcriptome data. Taken together, we urge that the prognostic model and first-hand clinical trial data generated in this study have important clinical practical significance for predicting and exploring the disease progression of COVID-19 patients.
ABSTRACT
SUMMARY: There are seven known coronaviruses that infect humans: four mild coronaviruses, including HCoV-229E, HCoV-OC43, HCoV-NL63 and HCoV-HKU1, only cause mild respiratory diseases, and three severe coronaviruses, including SARS-CoV, MERS-CoV and SARS-CoV-2, can cause severe respiratory diseases even death of infected patients. Both infection and death caused by SARS-CoV-2 are still rapidly increasing worldwide. In this study, we demonstrate that viral coding proteins of SARS-CoV-2 have distinct features and are most, medium and least conserved with SARS-CoV, MERS-CoV and the rest four mild coronaviruses (HCoV-229E, HCoV-OC43, HCoV-NL63 and HCoV-HKU1), respectively. Moreover, expression of host responsive genes (HRG), HRG-enriched biological processes and HRG-enriched KEGG pathways upon infection of SARS-CoV-2 shows slightly overlapping with SARS-CoV and MERS-CoV but distinctive to the four mild coronaviruses. Interestingly, enrichment of overactivation of neutrophil by HRGs is only and commonly found in infections of severe SARS-CoV-2, SARS-CoV and MERS-CoV but not in the other four mild coronaviruses, and the related gene networks show different patterns. Clinical data support that overactivation of neutrophil for severe patients can be one major factor for the similar clinical symptoms observed in SARS-CoV-2 infection compared to infections of the other two severe coronavirus (SARS-CoV and MERS-CoV). Taken together, our study provides a mechanistic insight into SARS-CoV-2 epidemic via revealing the conserved and distinct features of SARS-CoV-2, raising the critical role of dysregulation of neutrophil for SARS-CoV-2 infection. AVAILABILITY AND IMPLEMENTATION: All data sources and analysis methods related to this manuscript are available in the methods, supplementary materials and GEO database (https://www.ncbi.nlm.nih.gov/geo/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.