A comprehensive analysis of gene expression profiling data in COVID-19 patients for discovery of specific and differential blood biomarker signatures.

COVID-19 is a newly recognized illness with a predominantly respiratory presentation. Although initial analyses have identified groups of candidate gene biomarkers for the diagnosis of COVID-19, they have yet to identify clinically applicable biomarkers, so we need disease-specific diagnostic biomarkers in biofluid and differential diagnosis in comparison with other infectious diseases. This can further increase knowledge of pathogenesis and help guide treatment. Eight transcriptomic profiles of COVID-19 infected versus control samples from peripheral blood (PB), lung tissue, nasopharyngeal swab and bronchoalveolar lavage fluid (BALF) were considered. In order to find COVID-19 potential Specific Blood Differentially expressed genes (SpeBDs), we implemented a strategy based on finding shared pathways of peripheral blood and the most involved tissues in COVID-19 patients. This step was performed to filter blood DEGs with a role in the shared pathways. Furthermore, nine datasets of the three types of Influenza (H1N1, H3N2, and B) were used for the second step. Potential Differential Blood DEGs of COVID-19 versus Influenza (DifBDs) were found by extracting DEGs involved in only enriched pathways by SpeBDs and not by Influenza DEGs. Then in the third step, a machine learning method (a wrapper feature selection approach supervised by four classifiers of k-NN, Random Forest, SVM, Naïve Bayes) was utilized to narrow down the number of SpeBDs and DifBDs and find the most predictive combination of them to select COVID-19 potential Specific Blood Biomarker Signatures (SpeBBSs) and COVID-19 versus influenza Differential Blood Biomarker Signatures (DifBBSs), respectively. After that, models based on SpeBBSs and DifBBSs and the corresponding algorithms were built to assess their performance on an external dataset. Among all the extracted DEGs from the PB dataset (from common PB pathways with BALF, Lung and Swab), 108 unique SpeBD were obtained. Feature selection using Random Forest outperformed its counterparts and selected IGKC, IGLV3-16 and SRP9 among SpeBDs as SpeBBSs. Validation of the constructed model based on these genes and Random Forest on an external dataset resulted in 93.09% Accuracy. Eighty-three pathways enriched by SpeBDs and not by any of the influenza strains were identified, including 87 DifBDs. Using feature selection by Naive Bayes classifier on DifBDs, FMNL2, IGHV3-23, IGLV2-11 and RPL31 were selected as the most predictable DifBBSs. The constructed model based on these genes and Naive Bayes on an external dataset was validated with 87.2% accuracy. Our study identified several candidate blood biomarkers for a potential specific and differential diagnosis of COVID-19. The proposed biomarkers could be valuable targets for practical investigations to validate their potential.

Correlation of Low Levels of α-1 Antitrypsin and Elevation of Neutrophil to Lymphocyte Ratio with Higher Mortality in Severe COVID-19 Patients.

BACKGROUND: Variations in COVID-19 prevalence, severity, and mortality rate remain ambiguous. Genetic or individual differences in immune response may be an explanation. Moreover, hyperinflammation and dysregulated immune response are involved in the etiology of severe forms of COVID-19. Therefore, the aim of the present study was to analyze serum alpha-1 antitrypsin (AAT) levels, as an acute-phase plasma protein with immunomodulatory effect and neutrophil to lymphocyte ratio (NLR) as a marker of inflammation response in severe COVID-19 illness. METHODS: In this retrospective observational cohort study, 64 polymerase chain reaction (PCR) positive COVID-19 hospitalized patients were studied for AAT, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), troponin, complete blood count (CBC), random blood sugar, serum glutamate oxaloacetate transaminase (SGOT), serum glutamate pyruvate transaminase (SGPT), and arterial oxygen saturation (O2sat) at admission and during hospitalization. RESULTS: The results showed that hospitalized patients with COVID-19 had low serum levels of AAT and high CRP levels at the first days of hospitalization. In particular, the percentages of individuals with low, normal, and high AAT levels were 7.80%, 82.80%, and 9.40%, respectively, while high and low values of CRP accounted for 86.70% and 13.30% of patients. Most of the patients had an upward neutrophil to lymphocyte ratio (NLR) trend, with a higher mortality rate (p < 0.05) and troponin levels (p < 0.05). However, comorbidities, CRP alterations, ESR alterations, nonfasting blood sugar, SGOT, SGPT, O2sat, RBC, and PLT values were not significantly different between the NLR downward and upward trend groups. CONCLUSIONS: The current study revealed that severe COVID-19 patients had low serum AAT levels related to CRP values. Therefore, AAT response may be considered as a new mechanism by which some COVID-19 patients show immune dysregulation and more severe symptoms.