The paucity of studies available in the literature on brain tumors demonstrates that liquid biopsy (LB) is not currently applied for central nervous system (CNS) cancers. The purpose of this systematic review focused on the application of machine learning (ML) to LB for brain tumors to provide practical guidance for neurosurgeons to understand the state-of-the-art practices and open challenges. The herein presented study was conducted in accordance with the PRISMA-P (preferred reporting items for systematic review and meta-analysis protocols) guidelines. An online literature search was launched on PubMed/Medline, Scopus, and Web of Science databases using the following query: “((Liquid biopsy) AND (Glioblastoma OR Brain tumor) AND (Machine learning OR Artificial Intelligence))”. The last database search was conducted in April 2023. Upon the full-text review, 14 articles were included in the study. These were then divided into two subgroups: those dealing with applications of machine learning to liquid biopsy in the field of brain tumors, which is the main aim of this review (n = 8); and those dealing with applications of machine learning to liquid biopsy in the diagnosis of other tumors (n = 6). Although studies on the application of ML to LB in the field of brain tumors are still in their infancy, the rapid development of new techniques, as evidenced by the increase in publications on the subject in the past two years, may in the future allow for rapid, accurate, and noninvasive analysis of tumor data. Thus making it possible to identify key features in the LB samples that are associated with the presence of a brain tumor. These features could then be used by doctors for disease monitoring and treatment planning.

Glioblastoma (GBM) is a primary CNS tumor that is highly lethal in adults and has limited treatment options. Despite advancements in understanding the GBM biology, the standard treatment for GBM has remained unchanged for more than a decade. Only 6.8% of patients survive beyond five years. Telomerase, particularly the hTERT promoter mutations present in up to 80% of GBM cases, represents a promising therapeutic target due to its role in sustaining telomere length and cancer cell proliferation. This review examines the biology of telomerase in GBM and explores potential telomerase-targeted therapies. We conducted a systematic review following the PRISMA-P guidelines in the MEDLINE/PubMed and Scopus databases, from January 1995 to April 2024. We searched for suitable articles by utilizing the terms “GBM”, “high-grade gliomas”, “hTERT” and “telomerase”. We incorporated studies addressing telomerase-targeted therapies into GBM studies, excluding non-English articles, reviews, and meta-analyses. We evaluated a total of 777 records and 46 full texts, including 36 studies in the final review. Several compounds aimed at inhibiting hTERT transcription demonstrated promising preclinical outcomes; however, they were unsuccessful in clinical trials owing to intricate regulatory pathways and inadequate pharmacokinetics. Direct hTERT inhibitors encountered numerous obstacles, including a prolonged latency for telomere shortening and the activation of the alternative lengthening of telomeres (ALT). The G-quadruplex DNA stabilizers appeared to be potential indirect inhibitors, but further clinical studies are required. Imetelstat, the only telomerase inhibitor that has undergone clinical trials, has demonstrated efficacy in various cancers, but its efficacy in GBM has been limited. Telomerase-targeted therapies in GBM is challenging due to complex hTERT regulation and inadequate inhibitor pharmacokinetics. Our study demonstrates that, despite promising preclinical results, no Telomerase inhibitors have been approved for GBM, and clinical trials have been largely unsuccessful. Future strategies may include Telomerase-based vaccines and multi-target inhibitors, which may provide more effective treatments when combined with a better understanding of telomere dynamics and tumor biology. These treatments have the potential to be integrated with existing ones and to improve the outcomes for patients with GBM.