UpToDate: Glioma biomarker

Glioma biomarker

see also Glioblastoma biomarker.

Gliomas are difficult to classify precisely because of interobserver variability during histopathologic grading. Identifying biological signatures of each glioma subtype through protein biomarker profiling of tumor or tumor-proximal fluids is therefore of high priority. Such profiling not only may provide clues regarding tumor classification but may identify clinical biomarkers and pathologic targets for the development of personalized treatments.

In the past, differential proteomic profiling techniques have utilized tumor, cerebrospinal fluid, and plasma from glioma patients to identify the first candidate diagnostic, prognostic, predictive, and therapeutic response markers, highlighting the potential for glioma biomarker discovery. The number of markers identified, however, has been limited, their reproducibility between studies is unclear, and none have been validated for clinical use.

Technological advancements in methodologies for high-throughput profiling, which provide easy access, rapid screening, low sample consumption, and accurate protein identification, are anticipated to accelerate brain tumor biomarker discovery. Reliable tools for biomarker verification forecast translation of the biomarkers into clinical diagnostics in the foreseeable future 1).

Glioma shed extracellular vesicles (EVs), which invade the surrounding tissue and circulate within both the cerebrospinal fluid and the systemic circulation. These tumor-derived EVs and their content serve as an attractive source of biomarkers.

In a review, Hochberg et al., discuss the current state of the art of biomarkers for glioma with emphasis on their EV derivation 2).


A study identified an 18-cytokine signature for distinguishing glioma sera from normal healthy individual sera and also demonstrated the importance of their differential abundance in glioma biology 3).


Shi et al., from Hangzhou, Department of Neurosurgery, Changhai Hospital, Second Military Medical University, Shanghai. Department of Neurosurgery, Huai’an Second People’s Hospital, The Affiliated Huai’an Hospital of Xuzhou Medical University, Huai’an, China, extracted data sets from the Gene Expression Omnibus data set by using “glioma” as the keyword. Then, a coexpression module was constructed with the help of Weighted Gene Coexpression Network Analysis software. Besides, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the genes in these modules. As a result, the critical modules and target genes were identified. Eight coexpression modules were constructed using the 4,000 genes with a high expression value of the total 141 glioma samples. The result of the analysis of the interaction among these modules showed that there was a high scale independence degree among them. The GO and KEGG enrichment analyses showed that there was a significant difference in the enriched terms and degree among these eight modules, and module 5 was identified as the most important module. Besides, the pathways it was enriched in, hsa04510: Focal adhesion and hsa04610: Complement and coagulation cascades, were determined as the most important pathways. In summary, module 5 and the pathways it was enriched in, hsa04510: Focal adhesion and has 04610: Complement and coagulation cascades, have the potential to serve as glioma biomarkers 4).

References

1)

Kalinina J, Peng J, Ritchie JC, Van Meir EG. Proteomics of gliomas: initial biomarker discovery and evolution of technology. Neuro Oncol. 2011 Sep;13(9):926-42. doi: 10.1093/neuonc/nor078. Review. PubMed PMID: 21852429; PubMed Central PMCID: PMC3158015.

2)

Hochberg FH, Atai NA, Gonda D, Hughes MS, Mawejje B, Balaj L, Carter RS. Glioma diagnostics and biomarkers: an ongoing challenge in the field of medicine and science. Expert Rev Mol Diagn. 2014 May;14(4):439-52. doi: 10.1586/14737159.2014.905202. Review. PubMed PMID: 24746164; PubMed Central PMCID: PMC5451266.

3)

Nijaguna MB, Patil V, Hegde AS, Chandramouli BA, Arivazhagan A, Santosh V, Somasundaram K. An Eighteen Serum Cytokine Signature for Discriminating Glioma from Normal Healthy Individuals. PLoS One. 2015 Sep 21;10(9):e0137524. doi: 10.1371/journal.pone.0137524. eCollection 2015. PubMed PMID: 26390214.

4)

Shi T, Chen J, Li J, Yang BY, Zhang QL. Identification of key gene modules and pathways of human glioma through coexpression network. J Cell Physiol. 2018 Aug 1. doi: 10.1002/jcp.27059. [Epub ahead of print] PubMed PMID: 30067869.

UpToDate: Biomarker

Biomarker

Biological marker, generally refers to a measurable indicator of some biological state or condition. The term occasionally also refers to a substance whose presence indicates the existence of living organisms.

Biomarkers are often measured and evaluated to examine normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. Biomarkers are used in many scientific fields.


Extracellular vesicles secreted by human glioma cells contain a wealth of tumor-specific proteins and nucleic acids that can be isolated from patients with these neoplasms. Thus, EV contribute to the development of biomarkers, and additionally have certain therapeutic potential for possible use in neurooncology and neurosurgery 1).

see Molecular biomarker.

see Biochemical marker.

see Glioblastoma biomarkers.

see Red cell distribution width.

see Tumor marker.

Circulating microRNAs (miRNAs) are a new class of highly promising cancer biomarkers.

It is of great importance to seek further subclassifications in glioblastoma multiformebiomarkers, and new treatment modalities to make a significant change in survival for individuals 2).

Examples

1)

Santiago-Dieppa DR, Gonda DD, Cheung VJ, Steinberg JA, Carter BS, Chen CC. Extracellular Vesicles as a Platform for Glioma Therapeutic Development. Prog Neurol Surg. 2018;32:172-179. doi: 10.1159/000469689. Epub 2018 Jul 10. PubMed PMID: 29990983.
2)

Fekete B, Werlenius K, Örndal C, Rydenhag B. Prognostic factors for glioblastoma patients – a clinical population-based study. Acta Neurol Scand. 2016 Jun;133(6):434-41. doi: 10.1111/ane.12481. Epub 2015 Sep 11. PubMed PMID: 26358197.
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