Bioinformatics analyses of significant genes, related pathways, and candidate diagnostic biomarkers and molecular targets in SARS-CoV-2/COVID-19

Abstract

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infection is a leading cause of pneumonia and death. The aim of this investigation is to identify the key genes in SARS-CoV-2 infection and uncover their potential functions. We downloaded the expression profiling by high throughput sequencing of GSE152075 from the Gene Expression Omnibus database. Normalization of the data from primary SARS-CoV-2 infected samples and negative control samples in the database was conducted using R software. Then, joint analysis of the data was performed. Pathway and Gene ontology (GO) enrichment analyses were performed, and the protein-protein interaction (PPI) network, target gene - miRNA regulatory network, target gene - TF regulatory network of the differentially expressed genes (DEGs) were constructed using Cytoscape software. Identification of diagnostic biomarkers was conducted using receiver operating characteristic (ROC) curve analysis. 994 DEGs (496 up regulated and 498 down regulated genes) were identified. Pathway and GO enrichment analysis showed up and down regulated genes mainly enriched in the NOD-like receptor signaling pathway, Ribosome, response to external biotic stimulus and viral transcription in SARS-CoV-2 infection. Down and up regulated genes were selected to establish the PPI network, modules, target gene - miRNA regulatory network, target gene - TF regulatory network revealed that these genes were involved in adaptive immune system, fluid shear stress and atherosclerosis, influenza A and protein processing in endoplasmic reticulum. In total, ten genes (CBL, ISG15, NEDD4, PML, REL, CTNNB1, ERBB2, JUN, RPS8 and STUB1) were identified as good diagnostic biomarkers. In conclusion, the identified DEGs, hub genes and target genes contribute to the understanding of the molecular mechanisms underlying the advancement of SARS-CoV-2 infection and they may be used as diagnostic and molecular targets for the treatment of patients with SARS-CoV-2 infection in the future.

SARS-CoV-2 Infected Pediatric Cerebral Cortical Neurons: Transcriptomic Analysis and Potential Role of Toll-like Receptors in Pathogenesis

“…A previous work reported that JNK cascade was activated in influenza A virus-infected neurons, while a delayed p38 activation was visible in astrocytes [ 66 ]. JUN and FOS were downregulated, as found also in a transcriptomic analysis of nasopharyngeal swabs of SARS-CoV-2 positive compared to negative subjects [ 67 ]. Interestingly, JUN transcript was restricted at post transcriptional level by SARS-CoV-2 [ 68 ], also due to its short half-life [ 69 ].…”

Gene expression analysis of MCF7 cell lines of breast cancer treated with herbal extract of Cissampelos pareira revealed association with viral diseases

“…As previous studies confirmed that IGFBP5 plays role in key positions such as inducing cell adhesion, increasing cell survival and inhibiting cell migration in MCF-7 human breast cancer cells ( Sureshbabu et al, 2012 ) and reported as one of the significant genes of COVID-19 ( Vastrad et al, 2020 ). These outcomes confirm the key task of the Interleukin-3-mediated signaling pathway engaged in breast cancer, COVID-19, and treatments of similar infectious diseases offering new molecular therapeutic targets to improve fundamental drug agents.…”

Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets

“…The proteins encoded by the CXCL9 and CXCL10 genes are highly significant players in the so-called “cytokine storm” produced by the corona-virus infection 37 . In addition, PCSK5, ROCK1P1, RPLP1, and NDUFV1 were found to be differentially expressed between SARS-CoV-2 infected patients and non-infected individuals in the study of Vastrad et al 36 . These data confirm that features selected by AutoML are biologically relevant, some of them already confirmed, others emerging here for the first time.…”

“…These findings seem highly relevant biologically and are novel, showing the potential of the AutoML method to unravel new players in the pathological process. Among the common features, IFI27 was found to be up-regulated in the COVID-19 patients compared to the controls in the study of Huang et al 35 while the FAM83A has been found to be differentially expressed between SARS-CoV-2 infected patients and virus-free individuals 36 .…”