Search Results (96)

The understanding of medulloblastoma (MB) progression is limited by the lack of minimally invasive monitoring methods. Extracellular vesicles (EVs) carrying disease-specific signatures are promising for liquid biopsies, but clinical translation is hindered by inconsistent isolation techniques. This study compares small EVs (sEVs) and their proteomes from blood plasma (BP) and cerebrospinal fluid (CSF) in MB. Using ultrafiltration and size exclusion chromatography (UF-SEC), we isolated sEVs from pediatric patient samples. sEV proteins from matched CSF-BP samples from MB patients (MBCSF/MBBP), healthy BP controls (HCBP), and MB cell lines (MBCL) were analyzed by liquid chromatography-tandem mass spectrometry, subjected to Gene Ontology and Cytoscape analyses, and compared to published MB, CSF, and EV datasets. By optimizing UF-SEC for small volumes, we found that CSF-sEVs are smaller and elute in later SEC fractions. Proteins linked to the extracellular matrix (ECM) were enriched in MBCSF and MBCL, while integrin binding showed inconsistent patterns between MBCSF and MBBP. MBBP and HCBP showed no significant differences. Fourteen proteins from MB datasets were identified in our analysis and primarily enriched in CSF. These findings support CSF-sEVs as more informative than BP-sEVs for MB diagnosis and monitoring, emphasize the need for fluid-specific sEV isolation, and suggest that ECM components and integrins may mediate MB progression. Full article

Background: Glioblastoma multiforme (GBM) is the most common and aggressive primary brain tumour, associated with poor survival outcomes and significant clinical challenges. Conventional diagnostic methods, including MRI, CT, and histopathological analysis of tissue biopsies, are limited by their inability to reliably distinguish treatment effects from true tumour progression, often resulting in misdiagnosis and delayed intervention. Repeated tissue biopsies are also invasive and unsuitable for longitudinal monitoring. Liquid biopsy, a minimally invasive approach analysing tumour-derived material in biofluids such as blood and cerebrospinal fluid (CSF), offers a promising alternative. This review aims to evaluate current evidence on circulating biomarkers including circulating tumour cells (CTCs), circulating tumour DNA (ctDNA), microRNAs (miRNAs), extracellular vesicles (EVs), and proteins in GBM diagnosis and monitoring, and to assess the potential role of artificial intelligence (AI) in enhancing their clinical application. Methods: A narrative synthesis of the literature was undertaken, focusing on studies that have investigated blood- and CSF-derived biomarkers in GBM patients. Key aspects evaluated included biomarker biology, detection techniques, diagnostic and prognostic value, current technical challenges, and progress towards clinical translation. Studies exploring AI and machine learning (ML) approaches for biomarker integration and analysis were also reviewed. Results: Liquid biopsy enables repeated and minimally invasive sampling of tumour-derived material, reflecting the genetic, epigenetic, proteomic, and metabolomic landscape of GBM. Although promising, its translation into routine clinical practice is hindered by the low abundance of circulating biomarkers and lack of standardised collection and analysis protocols. Evidence suggests that combining multiple biomarkers improves sensitivity and specificity compared with single-marker approaches. Emerging AI and ML tools show significant potential for improving biomarker discovery, integrating multi-omic datasets, and enhancing diagnostic and prognostic accuracy. Conclusions: Liquid biopsy represents a transformative tool for GBM management, with the capacity to overcome limitations of conventional diagnostics and provide real-time insights into tumour biology. By integrating multiple circulating biomarkers and leveraging AI-driven approaches, liquid biopsy could enhance diagnostic precision, enable dynamic disease monitoring, and improve clinical decision-making. However, large-scale validation and standardisation are required before routine clinical adoption can be achieved. Full article

(1) Background: Choroid plexus papillomas (CPPs) are rare brain tumors that tend to occur in very young children. Mechanisms of CPP development remain unelucidated. Separately, the process of angiogenesis has been implicated in other primary brain tumors. We hypothesize that angiogenesis is a hallmark of CPP biology. This study aims to identify and validate angiogenic factors in CPPs. (2) Methods: Cerebrospinal fluid (CSF) and CPP tumor samples are collected. A multiplex immunoassay panel is used to identify differentially expressed cytokines in the CSF samples. Concurrently, patient-derived primary cell cultures and their supernatants are derived from CPP samples. Targeted proteome blot arrays and human umbilical vein endothelial cell (HUVEC) angiogenesis assays are used for validation studies. (3) Results: CSF profiling showed higher expressions of VEGF-A, MCP-1, MMP-1, TNF-α, and CD40L in CPP patient samples versus non-tumor controls. Next, assessment via online protein–protein network platforms reports that these cytokines are associated with endothelial cell regulation. Using an angiogenesis-focused approach, CPP-derived cell lines and supernatants showed similarly higher expressions of VEGF, MCP-1, and MMP-1. Next, sprouting of nodes and tubule formation were observed in HUVEC angiogenesis assay cultures when conditioned CPP cell culture media was added. (4) Conclusions: This proof-of-concept study demonstrates potential to explore angiogenesis in CPP. Full article

The effective suppression of inflammation using disease-modifying therapies is essential in the treatment of multiple sclerosis (MS). Anti-CD20 monoclonal antibodies are commonly used long-term as maintenance therapies, largely due to the lack of reliable biomarkers to guide dosing and evaluate treatment response. However, prolonged use increases the risk of infections and other immune-mediated side effects. The unique ability of brain-derived blood extracellular vesicles (EVs) to cross the blood–brain barrier and reflect the central nervous system (CNS) immune status has sparked interest in their potential as biomarkers. This study aimed to assess whether blood-derived L1CAM⁺ EVs could serve as biomarkers of treatment response to rituximab (RTX) in patients with relapsing-remitting MS (RRMS). Serum samples (n = 25) from the baseline (month 0) and after 6 months were analyzed from the RTX arm of the ongoing randomized clinical trial OVERLORD-MS (comparing anti-CD20 therapies in RRMS patients) and were compared with serum samples from healthy controls (n = 15). Baseline cerebrospinal fluid (CSF) samples from the same study cohort were also included. EVs from both serum and CSF samples were characterized, considering morphology, size, and concentration, using transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA). The immunophenotyping of EV surface receptors was performed using flow cytometry with the MACSPlex exosome kit, while label-free quantitative proteomics of EV protein cargo was conducted using a proximity extension assay (PEA). TEM confirmed the presence of EVs with the expected round morphology with a diameter of 50–150 nm. NTA showed significantly higher concentrations of L1CAM⁺ EVs (p < 0.0001) in serum total EVs and EBNA1⁺ EVs (p < 0.01) in serum L1CAM⁺ EVs at baseline (untreated) compared to in healthy controls. After six months of RTX therapy, there was a significant reduction in L1CAM⁺ EV concentration (p < 0.0001) and the downregulation of TNFRSF13B (p = 0.0004; FC = −0.49) in serum total EVs. Additionally, non-significant changes were observed in CD79B and CCL2 levels in serum L1CAM⁺ EVs at baseline compared to in controls and after six months of RTX therapy. In conclusion, L1CAM⁺ EVs in serum showed distinct immunological profiles before and after rituximab treatment, underscoring their potential as dynamic biomarkers for individualized anti-CD20 therapy in MS. Full article

Background: The treatment landscape for spinal muscular atrophy (SMA) has changed significantly with the approval of gene-based therapies such as nusinersen for adults with SMA (pwSMA). Despite their efficacy, high costs and treatment burden highlight the need for biomarkers to objectify or predict treatment response. This study aimed to identify such biomarkers. Methods: A proteomic analysis of cerebrospinal fluid (CSF) from pwSMA (n = 7), who either significantly improved (SMA Improvers) or did not improve in motor function (SMA Non-Improvers) under nusinersen therapy, was performed. Data are available via ProteomeXchange with identifier PXD065345. Candidate biomarkers—Neuronal Pentraxin 2 (NPTX2), Contactin 5 (CNTN5), and Anthrax Toxin Receptor 1 (ANTXR1)—were investigated by ELISA in serum and CSF from an independent pwSMA cohort (n = 14) at baseline, 2 and 14 months after therapy initiation. Biomarker concentrations were correlated with clinical outcomes. Additionally, NPTX2 was stained in spinal cord sections from a mild SMA mouse model (FVB.Cg-Smn1tm1Hung Tg(SMN2)2Hung/J). Results: CSF NPTX2 levels decreased in pwSMA after 14 months of nusinersen therapy, independent of clinical response. The change in NPTX2 serum levels over 14 months of nusinersen treatment correlated with the change in HFMSE during this period. CNTN5 and ANTXR1 showed no significant changes. In the SMA mouse model, NPTX2 immunoreactivity increased at motoneuron loss onset. Conclusions: NPTX2 emerges as a potential biomarker of treatment response to nusinersen in pwSMA suggesting its significant pathophysiological role in late-onset SMA, warranting further investigation. Full article

Background: Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, yet severe immune-related adverse events (irAEs) often necessitate immunotherapy discontinuation and cause life-threatening complications. Circulating plasma proteins, dynamically accessible and functionally linked to immunity, may predict and offer novel targets for irAEs. Methods: Leveraging multi-omics integration, we conducted bidirectional two-sample Mendelian randomization (MR) using protein quantitative trait loci (pQTLs) from 4998 plasma proteins and genome-wide association data of irAE phenotypes. A causal inference framework combining colocalization analysis, multivariable MR (MVMR) adjusting for body mass index (BMI) confounding, and mediation MR elucidated BMI-independent pathways. Systems biology approaches including tissue-specific expression profiling, pathway enrichment, and protein interaction network analysis revealed spatial and functional drivers of irAE pathogenesis. Results: Proteome-wide MR mapping identified eight plasma proteins (CCL20, CSF1, CXCL9, CD40, TGFβ1, CLSTN2, TNFSF12, TGFα) causally associated with all-grade irAEs, and five (CCL20, CCL25, CXCL10, ADA, TGFα) with high-grade irAEs. Colocalization prioritized CD40/TNFSF12 (all-grade) and ADA/CCL25 (high-grade) as therapeutic targets (PPH4 > 0.7). CXCL9/TNFSF12 (all-grade) and CCL25 (high-grade) exerted BMI-independent effects, suggesting intrinsic immune dysregulation mechanisms. Tissue-specific gene expression patterns, CSF1, TGFβ1 in lung, TNFSF12 in the ileum may explain organ-specific irAE vulnerabilities. High-grade irAEs correlated with compartmentalized immune dysregulation and IL-17/immunodeficiency pathway activation. Conclusions: This study establishes the causal atlas of plasma proteins in irAE pathogenesis, bridging biomarker discovery with actionable therapeutic targets. These advances align with next-generation immunotherapy goals: maximizing efficacy while taming the immune storm. Full article

To advance our understanding of multiple sclerosis (MS), accurate identification of protein expression profiles as biomarkers for MS in cerebrospinal fluid (CSF) is critical. However, proteomic studies investigating MS have yielded inconsistent findings due to variability in sample sizes, diagnostic criteria, and data processing methods. We aimed to tackle these challenges by performing a thorough meta-analysis of proteomics datasets sourced from multiple independent studies. We conducted a thorough database search to gather all relevant studies using appropriate keywords. We screened articles using defined inclusion and exclusion criteria, and finally, six studies were included. We retrieved and combined data from five CSF datasets for discovery and two additional datasets for validation in 368 MS patients and controls. After data preprocessing, we calculated Z-scores for all datasets and for the integrated dataset. We used logistic regression models using training and validation datasets. We identified 11 differentially expressed proteins in the integrated dataset, revealing significant alterations in key pathways involved in immune response, neuroinflammation, and synaptic function. Notably, IGKC exhibited strong diagnostic potential, with an AUROC of 0.81. These findings highlight the value of re-analysing publicly available proteomics data to develop robust biomarker panels for MS diagnosis. Full article

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. Full article

Background/Objectives: Canine intervertebral disc herniation (IVDH) is an important musculoskeletal pathology. Unlike in humans, IVDH mechanisms in dogs are underinvestigated from a system-level integrative omics point of view. The aim of this study was to identify key serum molecular players in canine IVDH. Methods: An integrative multi-omics approach combining high-resolution LC-MS-based untargeted metabolomics and tandem mass tag (TMT)-based proteomics was applied. Additionally, serum zinc concentration was determined by spectrophotometry. Results: Nineteen serum metabolites were differentially abundant in IVDH dogs. Metabolite analysis highlighted dysregulation in lipoic acid and branched-chain amino acid (BCAA) metabolism, with elevated levels of valine, leucine, and isoleucine in IVDH. These findings suggest disrupted energy, nitrogen, and neurotransmitter metabolism, potentially contributing to the IVDH pathophysiology. Additionally, lower serum uridine, possibly influenced by BCAA accumulation, was observed, indicating altered neuroinflammatory responses. ELISA validation confirmed elevated serum levels of zinc-α2-glycoprotein (ZAG), alpha-1-microglobulin/bikunin precursor (AMBP), and vitronectin (VTN) in IVDH, supporting immune modulation and neuroprotective mechanisms. Serum prekallikrein (KLKB1) and Protein C inhibitor (SERPINA5), involved in fibrin cloth formation, were found to be lowered in IVDH patients. Pathway enrichment revealed disturbances in aromatic amino acid biosynthesis, with elevated phenylalanine, tyrosine, and tryptophan influencing neurotransmission and inflammation. In addition, elevated serum Zn concentration emphasized its antioxidant importance in immune response, wound healing, and neuropathic pain signaling. Conclusions: Integration with our prior CSF multi-omics data reinforced the relevance of identified molecules in IVDH-associated neurodegeneration, inflammation, and repair processes. This study offers insight into potential diagnostic biomarkers and therapeutic targets for canine IVDH through serum-based molecular profiling. Full article

Background/Objectives: In recent years, interest in studying changes in cancer metabolites has resulted in significant advances in the metabolomics field. Glioblastoma remains the most aggressive and lethal brain malignancy, which presents with notable metabolic reprogramming. Methods: We performed literature research from the PubMed database and considered research articles focused on the key metabolic pathways altered in glioblastoma (e.g., glycolysis, lipid metabolism, TCA cycle), the role of oncometabolites and metabolic plasticity, and the differential expression of metabolites in glioblastoma. Currently used metabolomics approaches can be either targeted, focusing on specific metabolites and pathways, or untargeted, which involves data-driven exploration of the metabolome and also results in the identification of new metabolites. Data processing and analysis is of great importance and can be improved with the integration of machine learning approaches for metabolite identification. Results: Changes in α/β-glucose, lactate, choline, and 2-hydroxyglutarate were detected in glioblastoma compared with non-tumor tissues. Different metabolites such as fumarate, tyrosine, and leucine, as well as citric acid, isocitric acid, shikimate, and GABA were detected in blood and CSF, respectively. Conclusions: Although promising new technological and bioinformatic approaches help us understand glioblastoma better, challenges associated with biomarker availability, tumor heterogeneity, interpatient variability, standardization, and reproducibility still remain. Metabolomics research, either alone or combined with genomics or proteomics (i.e., multiomics) in glioblastoma, can lead to biomarker identification, tracking of metabolic therapy response, discovery of novel metabolites and pathways, and identification of potential therapeutic targets. Full article

Chitinase-3-like protein 1 (CHI3L1) has been implicated in multiple sclerosis (MS) pathology, yet its precise role remains unclear. To elucidate its involvement, we performed proteomic analysis of cerebrospinal fluid (CSF) from relapsing-remitting MS (RRMS) patients using two-dimensional difference gel electrophoresis (2D-DIGE). CHI3L1 emerged as the most upregulated protein in recurrent RRMS. ELISA confirmed significantly elevated CHI3L1 levels in recurrent RRMS and secondary progressive MS (SPMS) patients, with levels decreasing in steroid responders but increasing in non-responders. Immunohistochemistry of MS brain autopsies revealed CHI3L1 expression predominantly in mature oligodendrocytes. In an experimental autoimmune encephalomyelitis (EAE) model, CHI3L1 was highly expressed in the spinal cord, particularly in oligodendrocytes and microglia/macrophages. Functional studies demonstrated that recombinant CHI3L1 (rCHI3L1) protected oligodendrocytes from LPC-induced cell death by attenuating ER stress (GRP78, ORP150). Moreover, rCHI3L1 counteracted IFN-β- and PSL-mediated inhibition of oligodendrocyte differentiation. In microglia, rCHI3L1 suppressed LPS-induced proinflammatory markers (IL-1β, iNOS). In vivo, rCHI3L1 administration significantly mitigated EAE severity by reducing gliosis, demyelination, and axonal degeneration. These findings highlight CHI3L1 as a critical modulator of neuroinflammation and oligodendrocyte survival, positioning it as a promising therapeutic target for MS. Full article

Background and Objectives: Despite ongoing research and evolving diagnostic criteria, progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS) remain notoriously difficult to differentiate, largely due to their overlapping clinical presentations and the absence of definitive biomarkers. Materials and Methods: We provide a comprehensive review of cerebrospinal fluid (CSF) biomarkers, which have proven valuable in the diagnosis of other neurodegenerative conditions, and their application to PSP and CBS. Results: The most promising results derive from a combination of biomarkers associated with Parkinson’s disease, Alzheimer’s disease, and neurofilament light chain. Furthermore, CSF proteomics analysis offers valuable insights into the pathogenesis of PSP and CBS and could also contribute to accurate diagnosis. Conclusions: CSF biomarkers hold significant potential for improving the differential diagnosis of PSP and CBS. A stepwise combination approach—starting with CSF α-synuclein and neurofilament light chain, followed by amyloid-β42 and total and phosphorylated tau—may provide clinicians with a practical framework for distinguishing PSP and CBS from other neurodegenerative disorders. To advance this field, future efforts should prioritize large-scale, multicenter studies employing standardized methodologies to enhance the validity and reproducibility of biomarker-based diagnostics. Importantly, considering the frequent pathological overlap between PSP and CBS, future studies would greatly benefit from pathology-confirmed cohorts to ensure diagnostic accuracy and to better delineate biomarker profiles across these challenging conditions. Full article

Plexiform neurofibroma (PNF) is an immune cell-rich peripheral nerve sheath tumor that develops primarily in individuals with Neurofibromatosis Type 1 (NF1). Granulocyte-macrophage colony stimulating factor receptor-β (GM-CSFR-β^c) is a shared component of receptors for the cytokines GM-CSF, IL-3, and IL-5, ligands with immunomodulatory and tumor promoting roles. In the present study, we use genetically engineered mouse model of neurofibroma. We identified the expression of GM-CSFR-β^c and GM-CSFR-α on PNF cells and on macrophages and dendritic cells in the PNF, using the Nf1^f/f; DhhCre mouse model of neurofibroma formation. Genetic deletion of GM-CSFR-β^c in this model reduced the number of PNFs, which was associated with decreased numbers of tumor-associated Iba1+ macrophages and CD11c+ dendritic cells (DC), while loss of GM-CSFR-α had no effect. Deletion of GM-CSFR-α or GM-CSFR-β^c did not improve mouse survival or the structure of Remak bundles in peripheral nerves. Proteome analysis of tumor lysates showed altered levels of numerous cytokines after receptor loss, suggesting that the compensatory effects of other cyto/chemokines maintain a proinflammatory environment promoting neurofibroma. Thus, GM-CSFR-β^c signaling contributes modestly to neurofibroma formation, apparently independently of its ligand GM-CSF. Full article

Multiple system atrophy (MSA) is a progressive neurodegenerative synucleinopathy. Differentiating MSA from other synucleinopathies, especially in the early stages, is challenging because of its overlapping symptoms with other forms of Parkinsonism. Thus, there is a pressing need to clarify the underlying biological mechanisms and identify specific biomarkers for MSA. The metabolic profile of cerebrospinal fluid (CSF) is known to be altered in MSA. To further investigate the biological mechanisms behind the metabolic changes, we created a network of altered CSF metabolites in patients with MSA and analysed these changes using bioinformatic software. Acknowledging the limitations of metabolomics, we incorporated proteomic data to improve the overall comprehensiveness of the study. Our in silico predictions showed elevated ROS, cytoplasmic inclusions, white matter demyelination, ataxia, and neurodegeneration, with ATP concentration, neurotransmitter release, and oligodendrocyte count predicted to be suppressed in MSA CSF samples. Machine learning and dimension reduction are important multi-omics approaches as they handle large amounts of data, identify patterns, and make predictions while reducing variance without information loss and generating easily visualised plots that help identify clusters, patterns, or outliers. Thus, integrated multiomics and machine learning approaches are essential for elucidating neurodegenerative mechanisms and identifying potential diagnostic biomarkers of MSA. Full article

Background: The molecular biology of Huntington’s Disease (HD) has grown substantially, with pathological considerations extending to genetic modifiers, epigenetic changes, transcriptomics, the proteome, and the metabolome. The metabolome and proteome are especially intriguing in that they most directly reflect the functional state of the cellular environment, which may involve some combination of pathology as well as compensation. Methods: We assessed CSF proteomics from eight participants by their functional severity (TFC range 3–13), with 47 proteins having a minimum r-value of 0.7 and nominal p-values < 0.05. Results: Our exploratory data reveal correlations between progression and several processes including inflammation, ECM homeostasis and NAD⁺ metabolism. Conclusions: Consistently identified targets that correlate with phenotype or progression may have value, if validated, as enrichment tools in clinical trials and potentially as markers of therapeutic response. Full article