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Abstract
Glioblastoma multiforme (GBM) is one of the most aggressive and lethal forms of the primary brain tumors. With predominance of tumor heterogeneity and emergence of new subtypes, new approaches are needed to develop tissue-based markers for tumor typing or circulatory markers to serve as blood-based assays. Multi-omics data integration for GBM tissues would offer new insights on the molecular view of GBM pathogenesis useful to identify biomarker panels. On the other hand, mapping differentially expressed tissue proteins for their secretory potential through bioinformatics analysis or analysis of the tumor cell secretome or tumor exosomes would enhance our understanding of the tumor microenvironment and prospects for targeting circulatory biomarkers. In this review, the authors first present potential biomarker candidates for GBM that have been reported and then focus on plausible pipelines for multi-omic data integration to identify additional, high-confidence molecular panels for clinical applications in GBM.
Acknowledgements
The authors thank DN Reddy for providing supporting information for this manuscript.
Financial & competing interests disclosure
This work was supported financially by a project from the Indian Council of Medical Research (ICMR), New Delhi. MK Gupta is financially supported by a Senior Research Fellowship from the Council of Scientific and Industrial Research (CSIR), Government of India. RV Polisetty is supported by the ICMR, Government of India, New Delhi. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending or royalties.
No writing assistance was utilized in the production of this manuscript.
Key issues
Glioblastomas are one of the most malignant and aggressive forms of cerebral gliomas. Although they comprise a small percentage of all adult tumors, they are one of the major causes of cancer-related deaths and continue to have a dismal prognosis.
Among the genetic markers, IDH1 mutation in combination with MGMT promoter hypermethylation is emerging as a promising prognostic biomarker for glioblastoma multiforme (GBM).
GBM is characterized by extensive intra- and inter-tumor heterogeneity contributing variable tumor aggressiveness and is an important factor in understanding treatment response and overall outcome of malignancy. Currently, four distinct subtypes have been identified based on their gene expression and molecular signatures.
Protein-based distinction of GBM subtypes is an unmet clinical need as also plasma-based biomarkers for post-treatment surveillance and tumor recurrence. Several plasma/serum- and cerebrospinal fluid-based potential biomarkers have been described, but not yet been validated adequately in the context of specific clinical questions.
Using bioinformatics tools, proteins with secretory potential may be identified from differentially regulated protein datasets from tumor tissues or secretome of GBM cell lines. Integration of these with altered proteins from plasma/cerebrospinal fluid from GBM patients generates a valuable resource of potential biomarkers for targeted investigations compensating for the limitations of unbiased direct analysis of patient plasma.
Multidimensional and multi-omics datasets often show significant concordance. Integrating these requires a methodological framework to enable a thorough assessment of the tumor level molecular changes to identify high confidence discovery stage molecular panels to address specific clinical questions. Integrating these would enable a comprehensive monitoring of the observed tumor level changes and help to identify high confidence discovery stage molecular panels to address specific clinical questions.
Extending the proteomic changes covering enzymes to identification of downstream metabolic activity will further pave way to predictive metabolomics in GBM.