Recognition of Differentially Expressed Molecular Signatures and Pathways Associated with COVID-19 Poor Prognosis in Glioblastoma Patients

Abstract

Glioblastoma (GBM) is a type of brain cancer that is typically very aggressive and difficult to treat. Glioblastoma cases have been reported to have increased during COVID-19. The mechanisms underlying this comorbidity, including genomic interactions, tumor differentiation, immune responses, and host defense, are not completely explained. Therefore, we intended to investigate the differentially expressed shared genes and therapeutic agents which are significant for these conditions by using in silico approaches. Gene expression datasets of GSE68848, GSE169158, and GSE4290 studies were collected and analyzed to identify the DEGs between the diseased and the control samples. Then, the ontology of the genes and the metabolic pathway enrichment analysis were carried out for the classified samples based on expression values. Protein–protein interactions (PPI) map were performed by STRING and fine-tuned by Cytoscape to screen the enriched gene module. In addition, the connectivity map was used for the prediction of potential drugs. As a result, 154 overexpressed and 234 under-expressed genes were identified as common DEGs. These genes were found to be significantly enriched in the pathways involved in viral diseases, NOD-like receptor signaling pathway, the cGMP-PKG signaling pathway, growth hormone synthesis, secretion, and action, the immune system, interferon signaling, and the neuronal system. STAT1, CXCL10, and SAMDL were screened out as the top 03 out of the top 10 most critical genes among the DEGs from the PPI network. AZD-8055, methotrexate, and ruxolitinib were predicted to be the possible agents for the treatment. The current study identified significant key genes, common metabolic signaling networks, and therapeutic agents to improve our perception of the common mechanisms of GBM–COVID-19.

Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model

“…These findings require further investigation, and they could implicate common therapies between those diseases and GBM depending on the gene expression and proteomics profiles of the patient which improves personalized medicine. Our findings come along with Alzahrani et al findings who found pathways associated with COVID-19 poor prognosis in GBM patients [82]. BIOGRID interactions analysis showed that our predicted molecular features interact with GBM driver genes including PIK3R1, EGFR, TP53, RB1, and NF1 (S3D Fig) . We also investigated the mutations in GBM driver genes in TCGA data and found that most of the mutations are in GBM patients with low survival but nonsignificant p-values (S3A-S3C Fig).…”

Integration of Omics Data and Network Models to Unveil Negative Aspects of SARS-CoV-2, from Pathogenic Mechanisms to Drug Repurposing

“…Most of these studies were based on a similar pipeline relying on network topology. The identification of protein hubs has proven useful in prioritizing the candidate targets for drug treatments in which to invest time for further study and validation [ 23 , 24 , 25 , 26 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 89 , 90 , 91 , 92 , 93 , 94 ]. The saving of time certainly represents a vitally important element in emergency situations, such as the one experienced due to COVID-19.…”

“…In the same way, a role as a hub and drug target appeared recursive for some specific genes. In addition to ACE2, Interleukin-6 (IL6) [ 79 , 90 , 96 , 97 , 99 , 100 ], vascular endothelial growth factor A (VEGFA) [ 23 , 77 , 97 , 100 , 101 ], transcription factor p65 (RELA) [ 75 , 90 , 91 , 94 , 96 , 101 ], signal transducer and activator of transcription 1-alpha/beta (STAT1) [ 26 , 75 , 83 , 93 , 100 ], mitogen-activated protein kinase 1 (MAPK1) [ 16 , 91 , 94 , 96 , 100 ], MAPK8 [ 90 , 91 , 96 , 100 ], mitogen-activated protein kinase 3 (MAPK3) [ 91 , 94 , 97 ], proto-oncogene tyrosine-protein kinase Src (SRC) [ 26 , 91 , 97 ], epidermal growth factor receptor (EGFR) [ 90 , 91 , 97 ] and RAC-alpha serine/threonine-protein kinase (AKT1) [ 78 , 91 , 94 ] were among those best ranked following network topology, molecular docking and molecular dynamic analyses—all results that highlight the enrichment and involvement of the MAPK [ 23 , 100 , 101 ], PI3K-Akt [ 24 , ...…”

“…Nodes with a high value of this centrality maintain communication with groups of nodes and control the information flow that passes through them. In other words, this hub or bottleneck represents a fundamental element of connection in signaling pathways, thus becoming a candidate drug target, as reported in several studies focused on SARS-CoV-2 investigation [ 23 , 24 , 25 , 26 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 89 , 90 , 91 , 92 , 93 , 94 ].…”

Coronavirus Disease Pathophysiology: Biomarkers, Potential New Remedies, Comorbidities, Long COVID-19, Post Pandemic Epidemiological Surveillance

“…Alzahrani, Khan, and Ahmad from Saudi Arabia and India [19] analyzed genes potentially implicated in the increased incidence of glioblastoma (inducing significant changes in the immune system) during the COVID-19 pandemic. They delivered a list of genes differentially expressed upon glioblastoma and COVID-19, the profiling of biological processes, the protein-protein interaction network, and the list of selected compounds with high negative correlations for the COVID-19-glioblastoma association with characterized genes.…”