Glioblastoma biomarker

see Glioblastoma IDH Mutant.

MGMT promoter methylation in Glioblastoma

Epidermal growth factor receptor 3 in glioblastoma

More and more biomarkers continue to be identified in glioblastoma Glioblastoma patients. Such biomarkers are related with varying degrees of specificity to one or more of Glioblastoma's subtypes and, in many instances, may provide useful information about prognosis. Biomarkers fall into either the imaging or molecular category. Molecular biomarkers are identified by use of such platforms as genomics, proteomics, and metabolomics. In the future, biomarkers, either individually or in some combination, will more reliably identify the pathogenic type of Glioblastoma and determine choice of therapy ¹⁾.

Molecular biomarkers have become an integral part of tumor assessment in modern neuro-oncology and biomarker status now guides clinical decisions in some subtypes of gliomas, including anaplastic oligodendroglioma and glioblastoma in the elderly.

In gliomas molecular biomarkers are increasingly gaining diagnostic, prognostic and predictive significance. Determination of biomarker status after biopsy is important as not all patients are eligible for open tumor resection.

159 consecutively enrolled untreated gliomas were analyzed (94 glioblastomas, 2 gliosarcomas, 24 anaplastic astrocytomas, 10 oligo-tumors grade II/III, 20 grade II astrocytomas and 9 pilocytic astrocytomas). Transient morbidity was 2 %. Overall, the drop-out rate due to tissue contamination was 0.4 %. Median time from biopsy to histological and molecular genetic analyses was 3 and 5 days, respectively. Distributions of the respective biomarker status for tumor subgroups were consistent with the literature. The final histological diagnosis was changed/modified in 5/159 patients according to molecular findings. Treatment after molecular biopsy was highly

Molecular stereotactic biopsy is feasible and safe, can be implemented in daily clinical practice, improves diagnostic precision and enables personalized treatment ²⁾.

Advances in the understanding of the molecular biology of glioblastoma are being rapidly translated into innovative clinical trials, capitalizing on improved genomic, epigenetic, transcriptional, and proteomic characterization of glioblastomas as well as host factors, including the brain microenvironment and immune system interactions.

Biomarker discovery studies are needed to predict treatment outcome for patients with Glioblastoma ³⁾.

Aberrant gene expression and copy number alterations make it possible to identify four subtypes: classical, mesenchymal, proneural, and neural. More and more biomarkers continue to be identified in glioblastoma (Glioblastoma) patients. Such biomarkers are related with varying degrees of specificity to one or more of Glioblastoma's subtypes and, in many instances, may provide useful information about prognosis. Biomarkers fall into either the imaging or molecular category. Molecular biomarkers are identified by use of such platforms as genomics, proteomics, and metabolomics. In the future, biomarkers, either individually or in some combination, will more reliably identify the pathogenic type of Glioblastoma and determine choice of therapy.

Understanding the important biomarkers that play a role in Glioblastoma pathogenesis may also help clinicians in educating patients about prognosis, potential clinical trials, and monitoring response to treatments ⁴⁾.

In a study, dataset GSE50161 was used to construct a co-expression network for weighted gene co-expression network analysis. Two modules (dubbed brown and turquoise) were found to have the strongest correlation with glioblastoma (Glioblastoma). Functional enrichment analysis indicated that the brown module was involved in the cell cycle, DNA replication, and pyrimidine metabolism. The turquoise module was primarily related to circadian rhythm entrainment, glutamatergic synapses, and axon guidance. Hub genes were screened by survival analysis using The Cancer Genome Atlas and Human Protein Atlas databases and further tested using the GSE4290 and Gene Expression Profiling Interactive Analysis databases. The eight hub genes (NUSAP1, SHCBP1, KNL1, SULT4A1, SLC12A5, NUF2, NAPB, and GARNL3) were verified at both the transcriptional and translational levels, and these gene expression levels were significant based on the World Health Organization classification system. These hub genes may be potential biomarkers and therapeutic targets for the accurate diagnosis and management of Glioblastoma ⁵⁾.

A study of Zhao et al. aimed to identify novel tumor biomarkers with independent prognostic values in Glioblastomas. The DNA methylation profiles were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus database. Differential methylated genes (DMGs) were screened from Glioblastoma recurrence samples using limma package in R software. Functional enrichment analysis was performed to identify major biological processes and signaling pathways. Furthermore, critical DMGs associated with glioblastoma outcome were screened according to univariate and multivariate cox regression analysis. A risk score-based prognostic model was constructed for these DMGs and prediction ability of this model was validated in training dataset and validation dataset. In total, 495 DMGs were identified between recurrent samples and disease-free samples, including 356 significantly hypermethylated and 139 hypomethylated genes. Functional and pathway items for these DMGs were mainly related to sensory organ development, neuroactive ligand-receptor interaction, pathways in cancer, etc. Five genes with abnormal methylation level were significantly correlated with prognosis according to survival analysis, such as ALX1, KANK1, NUDT12, SNED1, and SVOP. Finally, the risk model provided an effective ability for prognosis prediction both in training and validation data set. They constructed a novel prognostic model for survival prediction of Glioblastomas. In addition, they identified five DMGs as critical prognostic biomarkers in Glioblastoma progression ⁶⁾.

Isocitrate dehydrogenase 1

Isocitrate dehydrogenase 2

p53

Platelet-derived growth factor receptor (PDGFR)

Phosphatase and tensin homolog (PTEN)

Phosphoinositide 3-kinase (PI3K)

1p/19q.

Zachariah M, Oliveira-Costa JP, Carter B, Stott SL, Nahed BV. Blood-Based Biomarkers for the Diagnosis and Monitoring of Gliomas. Neuro Oncol. 2018 May 9. doi: 10.1093/neuonc/noy074. [Epub ahead of print] PubMed PMID: 29746665 ⁷⁾.