Abstract
INTRODUCTION: The severity of coronavirus disease 2019 (COVID-19) was known to be affected by hyperinflammation. Identification of important proteins associated with hyperinflammation is critical. These proteins can be a potential target either as biomarkers or targets in drug discovery. Therefore, we combined enrichment analysis of these proteins to identify biological knowledge related to hyperinflammation. Moreover, we conducted transcriptomic data analysis to reveal genes contributing to disease severity. METHODS: We performed large-scale gene function analyses using gene ontology to identify significantly enriched biological processes, molecular functions, and cellular components associated with our proteins. One of the appropriate methods to functionally group large-scale protein-protein interaction (PPI) data into small-scale clusters is fuzzy K-partite clustering. We collected the transcriptomics data from GEO Database (GSE 164805 and GPL26963 platform). Moreover, we created a data set and analyzed gene expression using Orange Data-mining version 3.30. PPI analysis was performed using the STRING database with a confidence score >0.9. RESULTS: This study indicated that four proteins were associated with 25 molecular functions, three were associated with 22 cellular components, and one was associated with ten biological processes. All GOs of molecular function, cellular components, and 9 of 14 biological processes were associated with important cytokines related to the COVID-19 cytokine storm present in the resulting cluster. The expression analysis showed the interferon-related genes IFNAR1, IFI6, IFIT1, and IFIT3 were significant genes, whereas PPIs showed their interactions were closely related. CONCLUSION: A combination of enrichment using GOs and transcriptomic analysis showed that hyperinflammation and severity of COVID-19 may be caused by interferon signaling.