Bioinformatics and system biology approach to identify the influences of COVID-19 on metabolic unhealthy obese patients (preprint)

ABSTRACT

Objective: The severe acute respiratory syndrome coronavirus 2 has posed a significant challenge to health of individual. Increasing evidence shows that patients with metabolic unhealthy obesity (MUO) and COVID19 have severer complications and higher mortality rate. However, the molecular mechanisms underlying the association between MUO and COVID19 are poorly understood. We sought to implement transcriptomic analysis using bioinformatics and systems biology analysis approaches. Methods: Here, two datasets (GSE196822 and GSE152991) were employed to extract differentially expressed genes (DEGs) to identify common hub genes, shared pathways and candidate drugs and construct a gene disease network. Results: Based on the identified 65 common DEGs, the results revealed hub genes and essential modules. Moreover, common associations between MUO and COVID-19 were found. Transcription factors (TFs) and genes interaction, protein and drug interactions, and DEGs and miRNAs coregulatory network were identified. Furthermore, the gene-disease association were obtained and constructed. Conclusions: The shared pathogenic pathways are noted worth paying attention to. Several genes are highlighted as critical targets for developing treatments for and investigating the complications of COVID19 and MUO. Additionally, multiple genes are identified as promising biomarkers. We think this result of the study may help in selecting and inventing future treatments that can combat COVID-19 and MUO.