Background and aim: While many components of the ECM have been isolated and characterized, its modifications in the specific setting of GBMs have only been recently explored in the literature. The aim of this paper is to provide a systematic review on the topic and to assess the ECM’s role in shaping tumoral development. Methods: An online literature search was launched on PubMed/Medline and Scopus using the research string “((Extracellular matrix OR ECM OR matrix receptor OR matrix proteome) AND (glioblastoma OR GBM) AND (tumor invasion OR tumor infiltration))”, and a systematic review was conducted in accordance with the PRISMA-P guidelines. Results: The search of the literature yielded a total of 693 results. The duplicate records were then removed (n = 13), and the records were excluded via a title and abstract screening; 137 studies were found to be relevant to our research question and were assessed for eligibility. Upon a full-text review, 59 articles were finally included and were summarized as follows based on their focus: (1) proteoglycans; (2) fibrillary proteins, which were further subdivided into the three subcategories of collagen, fibronectin, and laminins; (3) glycoproteins; (4) degradative enzymes; (5) physical forces; (6) and glioma cell and microglia migratory and infiltrative patterns. Conclusions: Our systematic review demonstrates that the ECM should not be regarded anymore as a passive scaffold statically contributing to mechanical support in normal and pathological brain tissue but as an active player in tumor-related activity.

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.