Glioblastoma: Overview of Proteomic Investigations and Biobank Approaches for the Development of a Multidisciplinary Translational Network

Abstract

Glioblastoma is a highly aggressive, infiltrative brain tumor of the central nervous system (CNS). Its extensive molecular and biochemical heterogenicity hinders the identification of reliable biomarkers and therapeutic targets, thereby making prognosis and existing therapy ineffective. In recent years, breakthroughs in the use of proteomics on a range of biological samples, such as plasma, cerebrospinal fluid (CSF), tissues, brain cells, and exosomes, represent a potential improvement to GBM investigations. Mass spectrometry-based approaches represent an important technique in the characterization of the tumoral proteome, for the identification of differentially expressed proteins, and for studying altered molecular pathways involved in tumor stages. Proteomics studies advance our knowledge about GBM pathogenesis, the discovery of reliable diagnostic and prognostic markers, and therapeutic approaches, also. In this context, for the effective application of proteomics on GBM, it is mandatory to develop a translational network by integrating hospitals, biobanks, and research institutions into a single network, to enable a collaborative approach across disciplines, thereby enabling rapid translation to clinical application of new proteomic insights. Today, high-quality biobanks play a key role in enabling collaborative, ethically compliant research, supporting the effective application of proteomics in glioblastoma studies and the translation of discoveries into clinical practice. This review explores current trends in proteomics and GBM research, highlighting how leveraging biobank infrastructure and fostering institutional cooperation can drive the development of targeted pilot projects to enhance the impact and effectiveness of glioblastoma research.

Keywords: biobanks; biomarkers; brain tumors; clinical translation; glioblastoma; glioma; mass spectrometry; proteomics; translational network; tumor microenvironment.

Figure 2

Risk factors for glioblastoma. Stacked bar chart depicting the percentage distribution of age [47], sex [48], environmental exposure [53,54,55], and family history [56] among patients diagnosed with glioblastoma. Note that the risk factors are not mutually exclusive. Data synthesized from studies.

Figure 1

Reported incidence rates of glioblastoma (cases per 100,000 people) globally, in Italy, and in the United States, based on the most recent data from the NCI and ISS.

Figure 3

Distribution of PubMed articles on glioblastoma genomics, proteomics, and metabolomics. Yellow bars indicate total publications; red bars indicate publications from the last 10 years. Recent studies account for 78% of genomics, 80% of proteomics, and 91% of metabolomics publications.

Figure 4

PubMed-based distribution of glioblastoma-related articles (2014–2024), grouped by sample type (brain tissue, CSF, plasma, serum, urine, saliva) and categorized as human or animal studies. The histogram emphasizes the prevalence of tissue-based studies and highlights the relative underrepresentation of biofluid-based research.

Figure 5

Outline of the workflow in biobank-based biomedical research, illustrating the steps from sample collection and storage to analysis, result discovery, and the reuse of residual materials for new studies.

Figure 6

Evolution of the biobank concept. This infographic illustrates the progressive transformation from simple biological material collections to complex, regulated biobanking infrastructures integrating scientific research, data protection, and public health frameworks.