Next generation sequencing and molecular imaging identify EGFR mutation and amplification in a glioblastoma multiforme patient treated with an EGFR inhibitor: a case report

Glioblastoma multiforme: a multi-omics analysis of driver genes and tumour heterogeneity

Glioblastoma (GBM) is the most aggressive and common brain cancer in adults with the lowest life expectancy. The current neuro-oncology practice has incorporated genes involved in key molecular events that drive GBM tumorigenesis as biomarkers to guide diagnosis and design treatment. This study summarizes findings describing the significant heterogeneity of GBM at the transcriptional and genomic levels, emphasizing 18 driver genes with clinical relevance. A pattern was identified fitting the stem cell model for GBM ontogenesis, with an upregulation profile for MGMT and downregulation for ATRX, H3F3A, TP53 and EGFR in the mesenchymal subtype. We also detected overexpression of EGFR, NES, VIM and TP53 in the classical subtype and of MKi67 and OLIG2 genes in the proneural subtype. Furthermore, we found a combination of the four biomarkers EGFR, NES, OLIG2 and VIM with a remarkable differential expression pattern which confers them a strong potential to determine the GBM molecular subtype. A unique distribution of somatic mutations was found for the young and adult population, particularly for genes related to DNA repair and chromatin remodelling, highlighting ATRX, MGMT and IDH1. Our results also revealed that highly lesioned genes undergo differential regulation with particular biological pathways for young patients. This multi-omic analysis will help delineate future strategies related to the use of these molecular markers for clinical decision-making in the medical routine.

Identification of potential crucial genes and molecular mechanisms in glioblastoma multiforme by bioinformatics analysis

Glioblastoma multiforme (GBM) is the most common and malignant brain tumor of the adult central nervous system and is associated with poor prognosis. The present study aimed to identify the hub genes in GBM in order to improve the current understanding of the underlying mechanism of GBM. The RNA-seq data were downloaded from The Cancer Genome Atlas database. The edgeR package in R software was used to identify differentially expressed genes (DEGs) between two groups: Glioblastoma samples and normal brain samples. Gene Ontology (GO) functional enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed using Database for Annotation, Visualization and Integrated Discovery software. Additionally, Cytoscape and Search Tool for the Retrieval of Interacting Genes/Proteins tools were used for the protein-protein interaction network, while the highly connected modules were extracted from this network using the Minimal Common Oncology Data Elements plugin. Next, the prognostic significance of the candidate hub genes was analyzed using UALCAN. In addition, the identified hub genes were verified by reverse transcription-quantitative (RT-q) PCR. In total, 1,483 DEGs were identified between GBM and control samples, including 954 upregulated genes and 529 downregulated genes (P<0.01; fold-change >16) and these genes were involved in different GO terms and signaling pathways. Furthermore, CDK1, BUB1, BUB1B, CENPA and GNG3 were identified as key genes in the GBM samples. The UALCAN tool verified that higher expression level of CENPA was relevant to poorer overall survival rates. In conclusion, CDK1, BUB1, BUB1B, CENPA and GNG3 were found to be potential biomarkers for GBM. Additionally, ‘cell cycle’ and ‘γ-aminobutyric acid signaling’ pathways may serve a significant role in the pathogenesis of GBM.

Challenges in the Treatment of Glioblastoma: Multisystem Mechanisms of Therapeutic Resistance

Glioblastoma is one of the most lethal human cancers, with poor survival despite surgery, radiation treatment, and chemotherapy. Advances in the treatment of this type of brain tumor are limited because of several resistance mechanisms. Such mechanisms involve limited drug entry into the central nervous system compartment by the blood-brain barrier and by actions of the normal brain to counteract tumor-targeting medications. In addition, the vast heterogeneity in glioblastoma contributes to significant therapeutic resistance by preventing adequate control of the entire tumor mass by a single drug and by facilitating escape mechanisms from targeted agents. The stem cell-like characteristics of glioblastoma promote resistance to chemotherapy, radiation, and immunotherapy through upregulation of efflux transporters, promotion of glioblastoma stem cell proliferation in neurogenic zones, and immune suppression, respectively. Metabolic cascades in glioblastoma prevent effective treatments through the optimization of glucose use, the use of alternative nutrient precursors for energy production, and the induction of hypoxia to enhance tumor growth. In the era of precision medicine, an assortment of molecular techniques is being developed to target an individual's unique tumor, with the hope that this personalized strategy will bypass therapeutic resistance. Although each resistance mechanism presents an array of challenges to effective treatment of glioblastoma, as the field recognizes and addresses these difficulties, future treatments may have more efficacy and promise for patients with glioblastoma.