Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression

Abstract

Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disorders and GBM cells release glutamate and show an abnormality, but differ in cellular behavior. So, their etiology is not well understood, nor is it clear how CNS disorders influence GBM behavior or growth. This led us to employ a quantitative analytical framework to unravel shared differentially expressed genes (DEGs) and cell signaling pathways that could link CNS disorders and GBM using datasets acquired from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) datasets where normal tissue and disease-affected tissue were examined. After identifying DEGs, we identified disease-gene association networks and signaling pathways and performed gene ontology (GO) analyses as well as hub protein identifications to predict the roles of these DEGs. We expanded our study to determine the significant genes that may play a role in GBM progression and the survival of the GBM patients by exploiting clinical and genetic factors using the Cox Proportional Hazard Model and the Kaplan–Meier estimator. In this study, 177 DEGs with 129 upregulated and 48 downregulated genes were identified. Our findings indicate new ways that CNS disorders may influence the incidence of GBM progression, growth or establishment and may also function as biomarkers for GBM prognosis and potential targets for therapies. Our comparison with gold standard databases also provides further proof to support the connection of our identified biomarkers in the pathology underlying the GBM progression.

Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19

“…2 (A) represents the interaction between genes from the perspective of -value, while the heat map in Fig. 2 (B) shows the relationship between genes in terms of log fold change values [29] , [37] . Changing the significance level of differentially expressed gene products and the fold change cut-offs can reveal different results that imply different signaling pathways and functions involved.…”

“…In human tissues affected by COVID-19, gene expression analysis based on microarray and RNA-seq datasets can be a sensitive technique for studying global gene expression and identifying plausible molecular pathways that are activated, and this can be done with high sensitivity [29] . The transcriptome profile of diseased tissue was compared to the transcriptome profile of control (non-diseased) tissues to generate all of these microarrays and RNA-seq-based datasets.…”

Comprehensive Analysis of Alteration Landscape and Its Clinical Significance of Mitochondrial Energy Metabolism Pathway‐Related Genes in Lung Cancers

“…ADH1B was a low expression gene in our study. There are a large number of reports that mathematical models can be used to predict the survival prognosis of cancer patients [42][43][44][45][46], and many factors affect the prognosis. Gene expression can predict good or bad survival, but the exact survival interval is not accurate enough for mathematical models to predict.…”

Network based systems biology approach to identify diseasome and comorbidity associations of Systemic Sclerosis with cancers

“…Gene Ontology (GO) has an enormous community public database that gives a lot of controlled vocabularies (biological or biochemical terms), represents gene products depending on their features in the cell. [43]. It is a community-oriented database of gene ontologies to help organic annotation of significant genes [44] [45].…”