The DOI matches its Crossref record; 136 of the 136 references it cites were found in a registry or an index.
Diagnostic and Therapeutic Biomarkers in Glioblastoma: Current Status and Future Perspectives
Open accessAbstract
Glioblastoma (GBM) is a primary neuroepithelial tumor of the central nervous system, characterized by an extremely aggressive clinical phenotype. Patients with GBM have a poor prognosis and only 3–5% of them survive for more than 5 years. The current GBM treatment standards include maximal resection followed by radiotherapy with concomitant and adjuvant therapies. Despite these aggressive therapeutic regimens, the majority of patients suffer recurrence due to molecular heterogeneity of GBM. Consequently, a number of potential diagnostic, prognostic, and predictive biomarkers have been investigated. Some of them, such as IDH mutations, 1p19q deletion, MGMT promoter methylation, and EGFRvIII amplification are frequently tested in routine clinical practice. With the development of sequencing technology, detailed characterization of GBM molecular signatures has facilitated a more personalized therapeutic approach and contributed to the development of a new generation of anti-GBM therapies such as molecular inhibitors targeting growth factor receptors, vaccines, antibody-based drug conjugates, and more recently inhibitors blocking the immune checkpoints. In this article, we review the exciting progress towards elucidating the potential of current and novel GBM biomarkers and discuss their implications for clinical practice.
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Cited by
Mechanisms of Resistance and Current Treatment Options for Glioblastoma Multiforme (GBM)
Yalamarty, S. S. K., Filipczak, N., Li, X., Subhan, M. A., Parveen, F., Ataide, J. A., Rajmalani, B. A., & Torchilin, V. P. (2023) · Cancers · doi:10.3390/cancers15072116 · Cited by 293
Liquid biopsy: creating opportunities in brain space
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Metadata
- Authors
- Szopa, W., Burley, T. A., Kramer-Marek, G., & Kaspera, W.
- Venue
- BioMed Research International
- Publisher
- Wiley
- Published
- 2017
- Volume
- 2017
- Pages
- 1-13
- ISSN
- 1110-7243 (ISSN-L)
- 2314-6133
- 2314-6141
- OpenAlex
- W2590936958
- Cited by
- 320
- References
- 136
- FWCI
- 15.66
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
