Identification of potential mRNA panels for severe acute respiratory syndrome coronavirus 2 (COVID-19) diagnosis and treatment using microarray dataset and bioinformatics methods

Basavaraj Vastrad; Chanabasayya Vastrad; Anandkumar Tengli

doi:10.1007/s13205-020-02406-y

Identification of potential mRNA panels for severe acute respiratory syndrome coronavirus 2 (COVID-19) diagnosis and treatment using microarray dataset and bioinformatics methods

3 Biotech. 2020 Oct;10(10):422. doi: 10.1007/s13205-020-02406-y. Epub 2020 Sep 11.

Authors

Basavaraj Vastrad¹, Chanabasayya Vastrad², Anandkumar Tengli³

Affiliations

¹ Department of Pharmaceutics, SET`S College of Pharmacy, Dharwad, Karnataka 580002 India.
² Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad, Karnataka 580001 India.
³ Department of Pharmaceutical Chemistry, JSS College of Pharmacy, Mysuru and JSS Academy of Higher Education & Research, Mysuru, Karnataka 570015 India.

Abstract

The goal of the present investigation is to identify the differentially expressed genes (DEGs) between SARS-CoV-2 infected and normal control samples to investigate the molecular mechanisms of infection with SARS-CoV-2. The microarray data of the dataset E-MTAB-8871 were retrieved from the ArrayExpress database. Pathway and Gene Ontology (GO) enrichment study, protein-protein interaction (PPI) network, modules, target gene-miRNA regulatory network, and target gene-TF regulatory network have been performed. Subsequently, the key genes were validated using an analysis of the receiver operating characteristic (ROC) curve. In SARS-CoV-2 infection, a total of 324 DEGs (76 up- and 248 down-regulated genes) were identified and enriched in a number of associated SARS-CoV-2 infection pathways and GO terms. Hub and target genes such as TP53, HRAS, MAPK11, RELA, IKZF3, IFNAR2, SKI, TNFRSF13C, JAK1, TRAF6, KLRF2, CD1A were identified from PPI network, target gene-miRNA regulatory network, and target gene-TF regulatory network. Study of the ROC showed that ten genes (CCL5, IFNAR2, JAK2, MX1, STAT1, BID, CD55, CD80, HAL-B, and HLA-DMA) were substantially involved in SARS-CoV-2 patients. The present investigation identified key genes and pathways that deepen our understanding of the molecular mechanisms of SARS-CoV-2 infection, and could be used for SARS-CoV-2 infection as diagnostic and therapeutic biomarkers.

Keywords: Bioinformatics analysis; Biomarkers; Differentially expressed genes; Protein–protein interaction (PPI) network; SARS-CoV-2 infection.