Metabolic profiling during COVID-19 infection in humans: Identification of potential biomarkers for occurrence, severity and outcomes using machine learning.

BACKGROUND: After its emergence in China, the coronavirus SARS-CoV-2 has swept the world, leading to global health crises with millions of deaths. COVID-19 clinical manifestations differ in severity, ranging from mild symptoms to severe disease. Although perturbation of metabolism has been reported as a part of the host response to COVID-19 infection, scarce data exist that describe stage-specific changes in host metabolites during the infection and how this could stratify patients based on severity. METHODS: Given this knowledge gap, we performed targeted metabolomics profiling and then used machine learning models and biostatistics to characterize the alteration patterns of 50 metabolites and 17 blood parameters measured in a cohort of 295 human subjects. They were categorized into healthy controls, non-severe, severe and critical groups with their outcomes. Subject's demographic and clinical data were also used in the analyses to provide more robust predictive models. RESULTS: The non-severe and severe COVID-19 patients experienced the strongest changes in metabolite repertoire, whereas less intense changes occur during the critical phase. Panels of 15, 14, 2 and 2 key metabolites were identified as predictors for non-severe, severe, critical and dead patients, respectively. Specifically, arginine and malonyl methylmalonyl succinylcarnitine were significant biomarkers for the onset of COVID-19 infection and tauroursodeoxycholic acid were potential biomarkers for disease progression. Measuring blood parameters enhanced the predictive power of metabolic signatures during critical illness. CONCLUSIONS: Metabolomic signatures are distinctive for each stage of COVID-19 infection. This has great translation potential as it opens new therapeutic and diagnostic prospective based on key metabolites.

Differential expression of long noncoding RNA in hepatocellular carcinoma on top of chronic HCV and HBV infections.

Aim of the study: The task of long noncoding RNAs (lncRNAs) as a prospective goal for hepatocellular carcinoma (HCC) is a candidate for research. Several lncRNAs are involved in signal transduction, directing gene expression and epigenetic alteration in normal and cancer cells. Dysregulation of diverse lncRNAs has been involved in the pathogenesis and progression of different cancers including HCC. We aimed to investigate the differential expression of lncRNAs (aHIF, hPVT1, ANRIL) in HCC on top of chronic hepatitis C virus (HCV) and hepatitis B virus (HBV) infections. Material and methods: 182 participants were included: 85 patients with HCC in addition to 50 patients with cirrhosis on top of chronic HCV or HBV, and 47 healthy subjects as controls. HCC was diagnosed by triphasic computed tomography (CT). Detection of α-fetoprotein (AFP) and serological markers of HCVAb and HBsAg by enzyme-linked fluorescent immunoassay (ELFA) and quantitation of lncRNAs by real time PCR were applied. Results: Upregulation of ANRIL and hPVT1 and downregulation of aHIF were observed in patients with HCC on top of HCV and HBV vs. controls. Circulating aHIF could be of major diagnostic importance to discriminate HCC on top of HCV from cirrhotic patients with sensitivity 86.67% and specificity 91.89% whereas circulating hPVT1 had sensitivity 85.0% and specificity 84.62%; moreover ANRIL had AUC 0.902 and could discriminate HCC on top of HBV from cirrhotic patients. Conclusions: The differential expression of lncRNAs (ANRIL, hPVT1 and aHIF) might be of major worth in predicting the occurrence of HCC in cirrhotic patients related to chronic viral hepatitis and could be beneficial in the early management.