COVID-19 Biomarkers in Research and Associations with Comorbidities

Nikhita Gogate, Amanda Bell, Daniel Lyman, Edmund Cauley, Ashia Joseph, Robel Kahsay, Raja Mazumder
2020 Zenodo  
Signature molecules, including genes, proteins, target panels, and glycans termed as biomarkers, are becoming increasingly significant in the Coronavirus disease 2019 (COVID-19) pandemic. High-throughput molecular characterization technologies have greatly accelerated the development of tests for risk assessment, diagnosis, prognosis, disease monitoring, and therapeutic evaluation. The disease complexity emphasizes the need for biomarker characterization that can help stratify patients based on
more » ... y patients based on their variable clinical manifestations and the presence of comorbidities. Powered by an NCI ITCR funded, comprehensive data model built to capture cancer biomarker data (OncoMX), and incorporating crowdsourced data collection and integration techniques, we have efficiently harmonized COVID-19 biomarker data. As of now, we have 146 potential biomarkers for COVID-19. The majority of biomarkers are associated with the immune system (including complement factors, inflammatory modulators, and pro-inflammatory factors) as well as coagulation factors. These trends suggest vascular pathobiology of the COVID-19 disease. Utilizing this collated biomarker data, we propose to identify common features and attributes of COVID-19, with cancer and other metabolic syndromes. Preliminary analysis shows biomarkers such as ACE2, IL-6, IL-4, and IL-2 have similar expression profiles in COVID-19 and cancer. Furthermore, COVID-19 biomarkers such as D-dimer, neutrophil to lymphocyte ratio (NLR), C-reactive protein (CRP), and low-density lipoprotein (LDL) shed light on the underlying physiological or pathological association with metabolic syndrome, diabetes, and hyperlipidemia. The importance of this study lies in identifying specific biomarkers that can successfully stratify patients based on distinct clinical presentations and the presence of comorbidities. Exploration of these patterns will benefit researchers and diagnosticians alike.
doi:10.5281/zenodo.4267247 fatcat:fcygzmdzwvdxjhpr5wqdezyu24