Evaluation of Secretome Biomarkers in Glioblastoma Cancer Stem Cells: A
Bioinformatics Analysis
Abstract
Glioblastoma (GBM) is a malignant brain tumor that frequently occurs
alongside other central nervous systems (CNS) conditions. Glutamate
release and aberrant cellular behavior are shared features of both CNS
diseases and GBM cells. Neither their origin nor the ways in which CNS
disorders affect the development or behavior of GBM are well understood.
Using data from the Gene Expression Omnibus (GEO) and the Cancer Genome
Atlas (TCGA) datasets–where both healthy and cancerous samples were
analyzed–we used a quantitative analytical framework to identify
differentially expressed genes (DEGs) and cell signaling pathways that
might be related to GBM. Then, we performed gene ontology studies and
hub protein identifications to estimate the roles of these DEGs after
finding disease-gene connection networks and signaling pathways. Using
the GEPIA Proportional Hazard Model and the Kaplan-Meier estimator, we
widened our analysis to identify the important genes that may play a
role in both progression and the survival of patients with GBM. Totally,
890 DEGs, including 475 and 415 up- and down-regulated were identified,
respectively. Our results revealed SQLE, DHCR7,
delta-1 phospholipase C ( PLCD1), and MINPP1 genes
are high expression, and the Enolase 2 ( ENO2) and
hexokinase-1 ( HK1) genes are low expressions. Hence, our
findings suggest novel mechanisms that affect the occurrence of GBM
development, growth, and/or establishment and may also serve as
secretory biomarkers for GBM prognosis and possible targets for therapy.