Venn diagram of overlapped differentially expressed circRNAs among circRNAs of circRNA datasets, miRNA datasets and mRNA datasets.

a) Overlapped differentially expressed circRNAs among circRNAs of circRNA datasets, miRNA datasets and mRNA datasets (whole genes). b) Overlapped differentially expressed circRNAs among circRNAs of circRNA datasets, miRNA datasets and mRNA datasets (cytokine storm related mRNAs).</p

Using circRNA based therapeutics to mitigate cytokine storm syndrome induced by SARS-CoV-2.

Using circRNA based therapeutics to mitigate cytokine storm syndrome induced by SARS-CoV-2.</p

SARS-CoV-2 induced cytokine storm related circRNA-miRNA-mRNA regulatory axis.

SARS-CoV-2 induced cytokine storm related circRNA-miRNA-mRNA regulatory axis.</p

KEGG analysis of 15 cytokine storm related genes.

Non-coding RNAs (ncRNAs) can control the flux of genetic information; affect RNA stability and play crucial roles in mediating epigenetic modifications. A number of studies have highlighted the potential roles of both virus-encoded and host-encoded ncRNAs in viral infections, transmission and therapeutics. However, the role of an emerging type of non-coding transcript, circular RNA (circRNA) in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has not been fully elucidated so far. Moreover, the potential pathogenic role of circRNA-miRNA-mRNA regulatory axis has not been fully explored as yet. The current study aimed to holistically map the regulatory networks driven by SARS-CoV-2 related circRNAs, miRNAs and mRNAs to uncover plausible interactions and interplay amongst them in order to explore possible therapeutic options in SARS-CoV-2 infection. Patient datasets were analyzed systematically in a unified approach to explore circRNA, miRNA, and mRNA expression profiles. CircRNA-miRNA-mRNA network was constructed based on cytokine storm related circRNAs forming a total of 165 circRNA-miRNA-mRNA pairs. This study implies the potential regulatory role of the obtained circRNA-miRNA-mRNA network and proposes that two differentially expressed circRNAs hsa_circ_0080942 and hsa_circ_0080135 might serve as a potential theranostic agents for SARS-CoV-2 infection. Collectively, the results shed light on the functional role of circRNAs as ceRNAs to sponge miRNA and regulate mRNA expression during SARS-CoV-2 infection.</div

The cytoHubba plug-in in Cytoscape was used to search the list of top 10 genes from the PPI network with node degrees indicating hub differentially expressed genes, including STAT1, RSAD2, IFIT1, IFIT3, IFIT2, DDX58, OAS2, MX2, IFI44 and IFI44L.

The cytoHubba plug-in in Cytoscape was used to search the list of top 10 genes from the PPI network with node degrees indicating hub differentially expressed genes, including STAT1, RSAD2, IFIT1, IFIT3, IFIT2, DDX58, OAS2, MX2, IFI44 and IFI44L.</p

The SARS-CoV-2 induced cytokine storm related circRNA-miRNA-mRNA network visualized using Cytoscape software.

The circRNA-miRNA-mRNA network contains 81 nodes and 205 edges.</p

List of count of differentially expressed RNAs and their predicted targets.

List of count of differentially expressed RNAs and their predicted targets.</p

List of all softwares and tools utilized in the current study.

List of all softwares and tools utilized in the current study.</p

Gene ontology analysis of differentially expressed genes.

Top GO terms with lowest P-values in cellular component, molecular function, and biological process were shown, respectively.</p

Datasets used for retrieving expression profiles of circRNAs, miRNAs and mRNAs.

Datasets used for retrieving expression profiles of circRNAs, miRNAs and mRNAs.</p