Plasma extracellular vesicle sampling from high grade gliomas demonstrates a small RNA signature indicative of disease and identifies lncRNA RPPH1 as a high grade glioma biomarker.

Plasma extracellular vesicle sampling from high grade gliomas demonstrates a small RNA signature indicative of disease and identifies lncRNA RPPH1 as a high grade glioma biomarker. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Plasma extracellular vesicle sampling from high grade gliomas demonstrates a small RNA signature indicative of disease and identifies lncRNA RPPH1 as a high grade glioma biomarker. Jae Ho Han, Gabriel Wajnberg, Kathleen M. Attwood, Lindsay Noiles, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4693910/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: High grade gliomas (HGGs) and cells of the tumour microenvironment (TME) secrete extracellular vesicles(EVs) into the plasma that contain genetic and protein cargo, which function in paracrine signaling. Isolation of these EVs and their cargo from plasma could lead to a simplistic tool that can inform on diagnosis and disease course of HGG. Methods: In the present study, plasma EVs were captured utilizing a peptide affinity method (Vn96 peptide) from HGG patients and normal controls followed by next generation sequencing (NovaSeq6000) to define a small RNA (sRNA) signature unique to HGG. Results: Over 750 differentially expressed sRNA (miRNA, snoRNA, lncRNA, tRNA, mRNA fragments and non-annotated regions) were identified between HGG and controls. MiEAA 2.0 pathway analysis of the miRNA in the sRNA signature revealed miRNA highly enriched in both EV and HGG pathways demonstrating the validity of results in capturing a signal from the TME. Also revealed were several novel HGG plasma EV sRNA biomarkers including lncRNA RPPH1 (Ribonuclease P Component H1), RNY4 (Ro60-Associated Y4) and RNY5 (Ro60-Associated Y5). Furthermore, in paired longitudinal patient plasma sampling, RPPH1 informed on surgical resection (decreased on resection) and importantly, RPPH1 increased again on clinically defined progression. Conclusions: The present study supports the role of plasma EV sRNA sampling (and particularly RPPH1 ) as part of a multi-pronged approach to HGG diagnosis and disease course surveillance. High-Grade Glioma Extracellular vesicles Small RNA sequencing Biomarkers Lnc RPPH1 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background High-grade gliomas (HGGs) are the most common primary malignant brain tumour, definitively diagnosed with invasive surgery [ 1 , 2 ]. The 2021 World Health Organization (WHO) classifies HGG as isocitrate dehydrogenase wild-type ( IDH - WT ), versus IDH mutant tumours [ 3 , 4 ]. IDH mutation defines two subsets of tumour that differ in terms of patient demographic, genetic, and prognostic factors. The most common and more aggressive IDH-WT HGG tumours, which includes diffuse Grade 3 astrocytomas and Grade 4 Glioblastoma Multiforme (GBM) were the focus of this study. IDH-WT HGGs are universally fatal (median survival 14–18 months) despite multimodal therapy (surgical resection, radiation, chemotherapy) [ 5 , 6 ]. HGG progression is the rule, as these tumours are proficient at adapting to a hostile microenvironment and co-opting surrounding normal brain cells (astrocytes and neurons), immune cells (e.g., microglia, monocytes, and macrophages), and endothelial cells to promote survival and progression [ 7 – 9 ]. Recent evidence suggests that several modalities of cell-to-cell communication between HGG cells and those of the tumour microenvironment (TME) enable pro-tumorigenic features [ 7 , 10 – 13 ]. Secretion of phospholipid membrane-bound extracellular vesicles (EVs) serve as an efficient means of bidirectional communication between HGG and cells of the TME to mediate autocrine/paracrine signaling [ 9 , 12 – 23 ]. For example, HGG cells release EVs containing pro-angiogenic proteins such as vascular endothelial growth factor ( VEGF ) isoforms A and C, or fibroblast growth factor ( FGF ) that target the surrounding endothelium and promote tumour vascularity [ 24 – 29 ]. Alternatively, EVs released by normal astrocytes containing tumour-suppressive cargo have been shown to inhibit growth of HGG [ 11 ]. EVs (ranging in size from 30nm to 1000nm) are secreted into all biofluids such as blood and cerebrospinal fluid (CSF) and are subdivided according to their size and subcellular origin (exosomes, microvesicles, and apoptotic bodies) [ 21 , 30 , 31 ]. While EVs are produced by almost all cell-types [ 32 ], malignant HGG cancer cells have been shown to increase production of EVs and their secretion into plasma [ 33 ]. Despite the blood-brain barrier, EVs derived from HGG tumours have been detected in the plasma, most notably in the well-designed experiment using surgically resected tissue and plasma collected from patients treated with 5-aminolevulinic acid (5-ALA) [ 34 ]. Thus, the isolation of plasma EVs and elucidation of their cargo is an appealing non-invasive methodology (liquid biopsy) for informing on HGG. Numerous methods of EV isolation have been described in the literature to analyze various cancer pathologies [ 34 – 37 ]. The Vn96 peptide is a peptide affinity capture method developed for the capture of EVs in clinically relevant biofluids [ 18 – 20 , 33 , 38 – 41 ]. Numerous studies have shown captured Vn96-EVs from plasma contain canonical EV markers, and Vn96 has been utilized for the discovery of EV cargo-biomarkers in amyotrophic lateral sclerosis [ 39 ] and cancers, such as prostate [ 42 ], lung [ 43 ], and pancreatic ductal adenocarcinoma [ 44 ]. Non-coding RNAs (such as miRNA, snoRNA, lncRNA, piRNA, and others) contained in EVs of HGG plasma and those of cancers outside of the brain, have already been shown to be effective forms of tumour-associated cell-to-cell communication [ 27 , 29 , 34 , 45 – 48 ]. For example, the long non-coding RNA (lncRNA) ribonuclease P RNA component H1 ( RPPH1 ) has been shown to play an important role in multiple cancers (e.g., lung, colorectal cancer) [ 49 , 50 ]. Moreover, RPPH1 contained in EVs isolated from colon cancer has been shown to regulate M2 polarization in macrophages of the TME, promoting proliferation and metastases of colon cancer cells [ 49 ]. In HGG patient plasma-EVs, MALAT1 was found to promote tumour proliferation and chemoresistance [ 51 ]. HGG angiogenesis was mediated by EV-associated CCAT2 or HIF1A-AS [ 52 , 53 ], and immune regulation of the HGG TME by EV MROCKI or LNCARSR [ 54 , 55 ]. The abnormal expression levels of various miRNA isolated from the EVs of HGG patient plasma have been found to mediate tumour aggressiveness and correlate with overall survival. Low levels of miR-485-3p were shown to correlate with significantly worse survival in HGG patients [ 56 ]. Angiogenesis was regulated by EV miR-1, miR-9, or miR-148a-3p [ 57 – 59 ], and TME immune regulation by miR-451 and miR-21[ 17 ]. EV miR-1238, miR-135b, or miR-151a were found to be critical in acquired treatment resistance [ 60 – 62 ]. These and other studies establish an ever-growing list implicating EVs and their sRNA cargo in many aspects of HGG tumorigenesis and cancer as a whole. We aim to demonstrate that HGG-associated plasma-EVs obtained with peptide affinity capture can serve as promising non-invasive biomarkers of disease with the potential to interrogate pro-tumour crosstalk [ 9 , 21 , 24 , 36 , 63 – 68 ]. For the first time in HGG we have utilized peptide affinity capture of plasma-EVs to determine a sRNA signature that can inform on HGG in patient samples pre- and post-surgery. Our sRNA signature recapitulates previous data demonstrating the validity of our method and adds novel potential sRNA biomarkers. These data can be used to aid in the establishment of biomarkers of disease and potential therapeutic targets in the future. Material and Methods Patient and sample collection Ethical approval was obtained from the Research Ethics Board of Nova Scotia Health (REB#1023343). Adult patients (>18 years old) identified as having a suspected HGG underwent surgical resection or biopsy were recruited from the Queen Elizabeth II Health Sciences Centre following written, informed consent. All surgeries were carried out under general anesthesia with standard monitoring of vitals, neuronavigation, and sterile surgical technique. Tumour resections were completed through a craniotomy overlying the tumour region. Biopsies were completed via a small craniotomy or burrhole with stereotactic guidance, at the discretion of the attending neurosurgeon. Whole blood samples were obtained longitudinally from just prior to the initiation of surgery (n = 10), 18 +/- 1 days post-surgery (n = 6), and at the time of clinically defined progression (n = 5). Only samples from patients with confirmed histopathological diagnosis of HGG (2021 WHO Grade III and IV tumours) were subsequently analyzed. Blood was collected in Vacutainer EDTA-tubes (BD). Control non-cancer plasma was obtained from Innovative Research Inc. (Novi, MI). Blood was processed within 2 hours of collection by centrifugation at 1,500xg for 15 min at 22°C. The plasma fraction was stored at -80°C. All methods were carried out in accordance with the Canadian Research Tri-Council policy on ethical conduct for research involving humans (https://ethics.gc.ca/eng/policy-politique_tcps2-eptc2_2018.html). Patients included is this study had to have pathology verified HGG (Grade 3 or 4) and be IDH-WT. Patients with Grade 3 or 4 astrocytoma and IDH-mutations were excluded. Patients in whom long-term plasma collection was not feasible due to distance from primary care site were excluded. Both male and female patients were recruited, however this study is underpowered to detect differences in sex variables and was not explored as part of this paper. As this was not a clinical trial, patients were not randomized, and investigators were not blinded to whether a sample was a control or HGG sample. HGG tumour volume assessment The volume of pre-surgical and post-surgical gadolinium-enhancing tumour tissue was measured on T1-weighted magnetic resonance imaging (MRI) scans, using semi-automated, intensity-based image segmentation. T1-hyperintense blood products seen on unenhanced T1-weighted images were digitally removed from gadolinium-enhanced T1-weighted images and were not included in the measurement of gadolinium-enhancing tumour tissue. Triplicate measurements were made by a single observer, and median values were recorded. Tumours without visible gadolinium enhancement were assigned a volume of zero. Vn96-mediated EV isolation and Protein or RNA extraction . Plasma was thawed at room temperature and pre-cleared at 3,000 x g for 15 min. Peptide-affinity capture of EVs was performed following well established protocols [40, 41, 43, 69]. EV-RNA was extracted with the miRVana miRNA isolation kit (Invitrogen) following manufacturer’s protocol for total RNA. EV-Protein was precipitated from the organic fraction using acetone and solubilized with 8 M urea, 0.2% SDS and 1 M Tris-HCl, pH 6.8 similar to [70, 71]. Total RNA quantity and profile was assessed using Fragment Analyzer 5200 (Agilent Technologies Inc.). Western blot analysis EV-Protein samples (25 µg) were divided into two aliquots of equal volume and one sample prepared under reducing conditions with 10% β-mercaptoethanol and the other under non-reducing conditions. Samples were loaded onto Any KD Mini-Protean TGX Stain-Free Protein gels (Bio-Rad) and transferred to 0.45 µm polyvinylidene fluoride membranes. Membranes were blocked with 5% (w/v) skim milk in tris-buffered saline with 0.1 % Tween-20 (TBST). Primary antibodies were prepared in TBST with 5% skim milk: Hsc-70 (1:200, Santa Cruz Biotechnology Cat# SC-7298, RRID:AB_627761), CD63 (1:200, Santa Cruz Biotechnology Cat# SC-5275, RRID:AB_627877), FLOT1 (1:1000, Cell Signaling Technology, Cat #18634, RRID:AB_2773040), CD9 (1:200, Santa Cruz Biotechnology Cat# SC-59140, RRID: AB_1120766), and GRP94 (1:1000, New England Biolabs, Cat#2104S, RRID:AB_823506). Anti-mouse IgG (1:5,000, Jackson ImmunoResearch, Cat#115-035-003, RRID:AB_10015289), and anti-rabbit IgG (1:5,000, Jackson ImmunoResearch, Cat#111-035-003, RRID:AB_2313567) secondary antibodies were used as appropriate and conjugated to horse radish peroxidase. HEK 293T cells were obtained from ATCC (Cat# CRL-3216). HEK 293T whole cell lysate protein was used as a positive control, cell lines are tested monthly for Mycoplasma and only negative cell lines were utilized. sRNA sequencing and bioinformatics Next-generation sequencing was performed on EV-RNA (3-10 ng) using the NextFlex Small RNA sequencing kit v3 (Perkin Elmer) following manufacturers’ protocol. cDNA libraries were quantified on iSeq prior to loading on a NovaSeq 6000 (Illumina).Raw reads from sRNAseq were converted to fastq files and the adapter sequences (TGGAATTCTCGGGTGCCAAGG) were removed using TrimGalore (v0.6.5; https://github.com/FelixKrueger/TrimGalore ). Alignment was performed using STAR (v2.7.0f; [72], RRID:SCR_004463) and the GRCh38 human genome with the parameters “—outFilterScoreMinOverLread 0 --outFilterMatchNmin 16 --outFilterMatchNminOverLread 0 --outFilterMismatchNoverLmax 0.05 --alignIntronMax 1 --alignEndsType EndToEnd”. The resulting .bam files were passed through a counting method by chromosome location using the Bioconductor package derfinder (v1.18.9; RRID:SCR_006442; [73]). Finally, the chromosome positions with counts were annotated using multiple databases such as Gencode (v38; [74]), pirnaDB (v1.7.6; [75]), MINT tRNA fragment database (v2.0; [76]), and Mirbase (v21; [77]). R statistical environment (v4.2.2) was used to calculate the variance between the normalized expressed chromosome regions read counts with trimmed mean of M-values (TMM) normalization method and differential expression analysis was performed using the Bioconductor package edgeR (v3.18.1; RRID:SCR_006442; [78]). R was used to build the following complimentary figures: upset plots with upset function from ‘upsetR’; MDS plots were built with plotMDS function from ‘edgeR’ library and ggplot from ‘ggplot2’ library; and the volcano plots were built using ggplot from ‘ggplot2’library. A false discovery rate (FDR Benjamini-Hochberg adjustment) of less than or equal to 0.05 and a fold change of greater than 1 or less than -1 were considered significant for further analysis. Functional analysis and tissue specificity of miRNA Functional analysis of the differentially expressed (DE) mature miRNAs between control and HGG patient samples was facilitated by the miRNA enrichment analysis and annotation tool (miEAA 2.0) [79]. Over-representation analysis (ORA) was performed with the default statistical parameters (FDR Benjamini-Hochberg adjustment and significance level 0.05). Selected databases included “Localization (RNALocate)”, “Diseases (MNDR)”, and “Pathways (miRWalk)”. Results of the analysis showing significantly over- or under-represented categories were exported to Excel (Microsoft) spreadsheets and ordered by the number of observed miRNAs. Tissue enrichment analysis for DE miRNA was facilitated by miRNAtissueAtlas2 [80]. For each DE miRNA (n = 34) the median reads per million was downloaded for the tissue panel, including brain, bone, lung, liver, lymph, bowel, muscle, heart and kidney. Median reads per million for each miRNA was divided by the maximum expression for each specific tissue and data was visualized by a heatmap. Analysis of sRNA expression (RPPH1) in tissue and assessment of prognostic value The expression of sRNA in tissue were queried from cancer and normal tissue repositories from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) databases accessed through the UCSC Xena browser (http://xenabrowser.net) [81]. To determine the expression RPPH1 in normal tissue as compared with HGG tissue, the TCGA Target GTEx database was filtered to select from only normal brain tissue (GTEx) and IDH-WT HGG tissue (TCGA). RPPH1 expression was profiled with respect to tissue type and the raw data was displayed as a violin plot. Welch’s t-test was used to determine statistical significance. Kaplan-Meier (KM) survival analysis was performed using the TCGA low grade glioma and glioblastoma database. Through Xena, grade 3 and 4 IDH-WT HGG were selected. Furthermore, as the TCGA database used an antiquated classification scheme (denoting all grade 2 IDH-WT tumours as low-grade gliomas), those grade 2 samples with any known driver mutations/amplifications for HGG [3, 82–84] were included. Eight grade 2 samples with amplifications of EGFR , PDGFRA/B , MDM2/4 , or mutations in PTEN , TP53 , H3F3A , TERT , CDKN2A/B , RB1 , NF1 , PIK3CA , EGFR , PDGFRA/B , or PDGFA/B /C/D were reclassified as HGG. KM curve was generated to compare HGG patients with high RPPH1 expression to those with low RPPH1 expression. Statistical significance was displayed as a p- value utilizing the log-rank test. P- value < 0.05 was considered significant. Analysis of RPPH1 expression between pre-surgery, post-surgery and progression was calculated using ANOVA with multiple comparisons (p<0.05). Results Patient cohort characteristics Age and sex of control patient’s plasma samples, along with baseline age, sex, and select tumour characteristics of ten HGG patients are shown (Table 1 ). The mean age of patients with tumours at the time of surgery was 65 years, and eight patients were male. All tumours were IDH-WT HGG. Half of the tumours were found to have methylated MGMT promoter regions and seven were predominantly localized to the temporal lobe (Table 1 ). Table 1 Important patient and tumour characteristics of high grade glioma (HGG) patients and commercial, control plasma samples. *Anaplastic astrocytoma patient was IDH wild type. High Grade Glioma IDH WT (n = 10) Controls (n = 4) Age at diagnosis, years Mean (SEM) 65 (1.6) 49 (3.5) Median (range) 65 (57–74) Sex, no. Males (% of total) 8 (80%) 3 (75%) Females (% of total) 2 (20%) 1 (25%) Histopathology, no. Glioblastoma 9 N/A Anaplastic astrocytoma 1* N/A MGMT status Methylated 5 N/A Unmethylated 5 N/A IDH mutation status Mutant 0 N/A Wildtype 10 N/A Tumour region Frontal 2 N/A Temporal 7 N/A Parietal 0 N/A Occipital 1 N/A sRNAs are differentially expressed between control and HGG plasma EVs Following the International Society of Extracellular vesicle (ISEV) guidelines, we confirmed the capture of EVs by Vn96 in HGG plasma, by detecting canonical EV-protein markers using Western blot analysis [ 85 ]. For this we pooled HGG EV-protein in order to conserve these precious samples. As demonstrated in Fig. 1 A we detected canonical EV-protein markers Hsc-70, CD63, CD9 and FLOT1 in the pooled HGG EV sample and very low amounts of the non-EV protein GRP94. This demonstrates the capture of EVs from plasma of HGG patients, similar to previously reported [ 70 , 86 – 88 ]. Sequencing and bioinformatic analysis of sRNA isolated from plasma EVs of controls and those from HGG patients, identified 4147 regions mapped to the human genome. The vast majority of the regions were uniquely annotated regions, although multi-annotated and non-annotated regions were also identified (Fig. 1 B). The distribution and frequency of uniquely annotated and multi-annotated regions stratified by the type of annotated sRNA is shown (Fig. 1 C). Most uniquely annotated regions were mRNA fragments, followed by lncRNA fragments, and a smaller proportion consisted of uniquely annotated tRNA, miRNA, and snoRNA. Most multi-annotated regions showed shared sequences with mRNA fragments, followed by lncRNA (Fig. 1 C). Considering uniquely annotated and multi-annotated miRNA, there were 542 miRNAs sequenced. Multi-dimensional scaling (MDS) plot shows control samples appearing to cluster distinctly from the HGG samples, suggesting dissimilarity between these two groups (Fig. 2 A). Differential expression (DE) analysis was performed and identified 789 regions, that passed statistical parameters, namely, false discovery rate (FDR) 1 or < -1 (Fig. 2 B). To better understand the complexity of DE regions, volcano plots were generated for the most relevant annotated sRNA species including miRNA (Fig. 2 C), snoRNA (Fig. 2 D), lncRNA full length/fragments (Fig. 2 E), mRNA fragments (Fig. 2 F), tRNA (Fig. 2 G), and for regions that have no known annotation (Fig. 2 H). Functional analysis of differentially expressed miRNA reveal associations with HGG and pathways involved in HGG pathogenesis Of the 528 miRNAs sequenced, 34 mature miRNAs were found to be significantly DE between control and HGG samples (Table 2 ). Considering the direction of fold change, 22 mature miRNAs were found to be enriched in HGG, while 12 were found to be depleted in HGG (Table 2 ). Table 2 Differentially expressed mature miRNA between control and HGG. Mature miRNAs are sorted by increasing FDR. Enriched in HGG plasma EVs-Mature ID Log 2 Fold change False Discovery Rate hsa-miR-451a 2.502 0.001 hsa-miR-200a-3p 7.071 0.003 hsa-miR-4511 6.511 0.004 hsa-miR-3648 6.057 0.005 hsa-miR-6803-3p 5.448 0.006 hsa-miR-218-5p 6.614 0.008 hsa-miR-223-3p 1.365 0.009 hsa-miR-6741-3p 7.868 0.014 hsa-miR-197-3p 2.062 0.015 hsa-miR-122-5p 2.334 0.017 hsa-miR-504-5p 7.175 0.021 hsa-miR-4755-3p 7.099 0.026 hsa-miR-143-3p 1.354 0.026 hsa-miR-1-3p 1.445 0.028 hsa-miR-328-3p 1.675 0.029 hsa-miR-4755-5p 6.938 0.030 hsa-miR-148b-3p 1.168 0.033 hsa-miR-16-5p 1.436 0.037 hsa-miR-485-3p 2.221 0.045 hsa-let-7i-5p 1.127 0.045 hsa-let-7i-3p 1.127 0.045 hsa-miR-7-5p 1.204 0.049 Depleted in HGG plasma EVs-Mature ID hsa-miR-4536-3p -6.768 0.002 hsa-miR-31-5p -5.014 0.003 hsa-miR-6866-3p -6.427 0.004 hsa-miR-223-5p -1.330 0.012 hsa-miR-320b -1.330 0.012 hsa-miR-320a-3p -1.018 0.014 hsa-miR-320d -2.022 0.015 hsa-miR-320c -1.456 0.017 hsa-miR-6881-3p -6.093 0.022 hsa-miR-887-3p -5.899 0.026 hsa-miR-184 -3.872 0.040 hsa-miR-484 -1.110 0.042 Utilizing miEAA 2.0, over-representation analysis (ORA) indicated significant over-representation of the 34 mature miRNAs in categories and subcategories associated with HGGs, cancer, and EVs (Table 3 ). Additionally, ORA revealed over-representation of the 34 mature miRNAs in categories associated with hallmarks of HGG pathogenesis including angiogenesis, proliferation/invasion (Table 4 ), and their relevant signaling pathways. For the full ORA output see Table S1 . In order to potentially understand the tissue origin of our DE miRNA, we utilized Tissue Atlas 2.0 (TissueAtlas (uni-saarland.de)). As shown in Fig. 3 a number of our DE miRNA have enriched expression in brain (miR-485-3p, miR-504-5p, miR-6881-3p and miR-184) and bone (miR-4511, miR-223-5p, miR-4755-5p, miR-6741-3p, and miR-4755-3p). Table 3 Significantly enriched categories related to localization and disease obtained utilizing ORA (miEAA 2.0) [ 79 ] for 34 DE miRNAs when comparing control to high grade glioma patients. Importantly, this demonstrates with functional annotation and pathway analysis of our DE miRNAs, added confidence in the sRNA signature (especially the miRNA contribution), which are over-represented in subcategories for EVs and HGG. Category Subcategory Enrichment False Discovery Rate Observed miRNA Localization (RNALocate) Microvesicle over-represented 0.000074 28 (hsa-miR-451a; hsa-miR-200a-3p; hsa-miR-4511; hsa-miR-3648; hsa-miR-218-5p; hsa-miR-223-3p; hsa-miR-223-5p; hsa-miR-197-3p; hsa-miR-122-5p; hsa-miR-504-5p; hsa-miR-4755-3p; hsa-miR-143-3p; hsa-miR-1-3p; hsa-miR-328-3p; hsa-miR-148b-3p; hsa-miR-16-5p; hsa-miR-485-3p; hsa-let-7i-5p; hsa-let-7i-3p; hsa-miR-7-5p; hsa-miR-31-5p; hsa-miR-320b; hsa-miR-320a-3p; hsa-miR-320d; hsa-miR-320c; hsa-miR-887-3p; hsa-miR-184; hsa-miR-484) Diseases (MNDR) Cancer over-represented 0.000401 27 (hsa-miR-451a; hsa-miR-200a-3p; hsa-miR-4511; hsa-miR-218-5p; hsa-miR-223-3p; hsa-miR-223-5p; hsa-miR-197-3p; hsa-miR-122-5p; hsa-miR-504-5p; hsa-miR-4755-3p; hsa-miR-143-3p; hsa-miR-1-3p; hsa-miR-328-3p; hsa-miR-4755-5p; hsa-miR-148b-3p; hsa-miR-16-5p; hsa-miR-485-3p; hsa-let-7i-3p; hsa-miR-7-5p; hsa-miR-31-5p; hsa-miR-320b; hsa-miR-320a-3p; hsa-miR-320d; hsa-miR-320c; hsa-miR-887-3p; hsa-miR-184; hsa-miR-484) Diseases (MNDR) Glioblastoma over-represented 0.000001 26 (hsa-miR-451a; hsa-miR-200a-3p; hsa-miR-4511; hsa-miR-218-5p; hsa-miR-223-3p; hsa-miR-223-5p; hsa-miR-197-3p; hsa-miR-122-5p; hsa-miR-504-5p; hsa-miR-143-3p; hsa-miR-1-3p; hsa-miR-328-3p; hsa-miR-148b-3p; hsa-miR-16-5p; hsa-miR-485-3p; hsa-let-7i-5p; hsa-let-7i-3p; hsa-miR-7-5p; hsa-miR-31-5p; hsa-miR-320b; hsa-miR-320a-3p; hsa-miR-320d; hsa-miR-320c; hsa-miR-887-3p; hsa-miR-184; hsa-miR-484) Diseases (MNDR) Malignant glioma over-represented 0.000074 22 (hsa-miR-451a; hsa-miR-200a-3p; hsa-miR-218-5p; hsa-miR-223-3p; hsa-miR-223-5p; hsa-miR-197-3p; hsa-miR-122-5p; hsa-miR-504-5p; hsa-miR-143-3p; hsa-miR-1-3p; hsa-miR-328-3p; hsa-miR-148b-3p; hsa-miR-16-5p; hsa-miR-485-3p; hsa-let-7i-5p; hsa-miR-7-5p; hsa-miR-31-5p; hsa-miR-320b; hsa-miR-320a-3p; hsa-miR-320c; hsa-miR-184; hsa-miR-484) Localization (RNALocate) Exosome over-represented 0.000043 16 (hsa-miR-451a; hsa-miR-200a-3p; hsa-miR-218-5p; hsa-miR-223-3p; hsa-miR-197-3p; hsa-miR-122-5p; hsa-miR-143-3p; hsa-miR-328-3p; hsa-miR-148b-3p; hsa-miR-16-5p; hsa-let-7i-5p; hsa-miR-7-5p; hsa-miR-31-5p; hsa-miR-320a-3p; hsa-miR-887-3p; hsa-miR-484) Table 4 Significantly enriched categories related to angiogenesis obtained via ORA (miEAA 2.0) [ 79 ] for 34 DE miRNAs when comparing control to high grade glioma patients. This demonstrates the potential role for EV-mediated miRNA transport to contribute to cell to cell communication in the TME, by regulating new blood vessel formation or proliferation and invasion. Category Subcategory Enrichment False Discovery Rate Observed miRNA Angiogenesis Pathways (miRWalk) P00005 Angiogenesis over-represented 0.0010 18 Pathways (miRWalk) P00021 FGF signaling pathway over-represented 0.0005 17 Pathways (miRWalk) P00056 VEGF signaling pathway over-represented 0.0023 14 Pathways (miRWalk) P04393 Ras Pathway over-represented 0.0024 14 Pathways (miRWalk) P00057 Wnt signaling pathway over-represented 0.0005 19 Proliferation and Invasion Pathways (miRWalk) P00018 EGF receptor signaling pathway over-represented 0.0013 16 Pathways (miRWalk) P00034 Integrin signalling pathway over-represented 0.0029 16 Pathways (miRWalk) hsa04350 TGF beta signaling pathway over-represented 0.0067 14 Pathways (miRWalk) P00048 PI3 kinase pathway over-represented 0.0088 13 Pathways (miRWalk) P00012 Cadherin signaling pathway over-represented 0.0226 11 Pathways (miRWalk) WP422 MAPK Cascade over-represented 0.0025 10 SnoRNA, lncRNA, and Y-RNA are differentially expressed between control and HGG plasma-EVs Also detected were DE sRNA species other than miRNA, for which pathway analysis was unavailable. As shown in Fig. 2 D, seven snoRNA were found enriched in HGG whereas nine were found depleted. For example, members of the SNORD3 family ( SNORD3A , 3B , 3C , 3D ), SNORD14B , SNORD42A , and SNORD89 were found to be enriched in HGG. In addition, 54 enriched and 103 depleted annotated regions to lncRNA full length/fragments were detected (Fig. 2 E). Since the library preparation kit used was designed for small RNA most of the regions annotated for lncRNA (and similarly mRNA) were only fragments of the entire transcript. This is similar to the output previously reported by others [ 47 , 89 , 90 ]. However, in some cases, where the full-length functional lncRNA transcript is short, a large portion of the transcript was detected and statistically significant differences in these lncRNA were sequenced. For example, for lncRNAs MALAT1 , and RPPH1 , large portions of the transcript were sequenced (~ 40% and ~ 100% respectively) and these were enriched in HGG. In certain cases, particularly in cancer, DE of fragments of sRNA may be clinically important. Like other studies [ 91 , 92 ], where cell-death effector fragments of RNY5 and tumour-suppressive fragments of RNY4 were detected in EVs from cancer cells, we detected enrichment in the 5’ (31 nt) effector region of RNY5 in HGG (Fig. 4 A, B). RNY4 , was also found to be enriched in HGG plasma-EVs, but unlike RNY5 , there was no significant difference in the proportions of RNY4 fragments seen between HGG and controls (Fig. 4 C). Examples of additional DE lncRNA and Y-RNA are shown in Table 5 . Table 5 Differentially expressed snoRNA, lncRNA, and Y-RNA between control and high grade glioma. snoRNA, lncRNA, and Y-RNA are sorted by increasing FDR Enriched In HGG plasma EVs-ID Log 2 Fold change False Discovery Rate snoRNA SNORD3B 4.06 6.00x10 − 11 SNORD3C 4.07 6.00x10 − 11 SNORD3D 4.06 6.0x10 − 11 SNORD3A 3.63 2.22x10 − 9 SNORD14B 6.05 0.0265 SNORD42A 4.26 0.0002 SNORD89 2.10 0.0009 lncRNA MALAT1 6.77 0.0069 RPPH1 1.69 0.0007 RP1-283E3.8 7.56 0.0092 Y-RNA RNY5 1.63 0.0012 RNY4 1.29 0.0170 Depleted in HGG plasma EVs-ID snoRNA SNORA62 -6.67 0.0002 SNORD64 -8.19 0.0021 SNORD67 -5.11 0.0037 SNORA5C -6.63 0.0086 SNORD111 -5.10 0.0095 SNORA79B -5.86 0.0166 SNORD11 -4.67 0.0233 SNORD44 -1.86 0.0334 SNORD49A -1.57 0.0355 SNORD32A -2.60 0.0440 lncRNA RP11-306O13.1 -8.29 0.0002 LINC02067 -6.91 0.0020 CTD-2651B20.7 -2.04 0.0024 CTD-2651B20.6 -2.04 0.0025 HELLPAR -6.45 0.0081 RP1-236J16.2 -5.89 0.0085 NORAD -5.99 0.0135 TAGAP-AS1 -5.63 0.0145 GPR176-D -5.96 0.0231 CYTOR -5.40 0.0293 Small RNA profiling shows differences between pre- and post-surgery HGG plasma-EVs that may signify volume of residual tumour remaining post-surgery We sought to see changes in sRNA profiles following surgery for HGG resection and interpret any differences in pre- and post-surgery profiles, in relation to the volume of tumour remaining post-surgery. Pre- and post-surgery plasma-EV samples were compared from six matched HGG patients. We first performed MDS to visualize global changes in sRNA signatures between pre- and post-surgery samples. As visualized on a MDS plot, sequencing of sRNA isolated from plasma-EVs revealed heterogenous changes in expression levels of sRNA when comparing matched pre- and post-surgery samples (Fig. 5 A). Although this analysis was complicated by the low sample size and various extents of resection, there appeared to be substantial, but heterogenous changes in four of the six HGG paired samples. Two pairs of samples did not show a marked change and were found clustered together as highlighted in the red circle. Interestingly, these two patients showed a high volume of post-surgical residual tumour (Fig. 5 B). Small RNA profiling of plasma-EVs appeared to be influenced by the extent of tumour remaining after surgery, with larger tumour volumes remaining post-surgery being associated with minimal changes in sRNA profiles, when comparing matched pre- and post-surgery samples. Differential expression analysis, with the same statistical parameters described previously, revealed only 23 regions DE between pre- and post-surgery samples (Fig. 5 C). For a list of DE sRNA in pre- and post-surgery plasma-EV comparison, see Table S2 . Post-surgical trends in sRNA identified as significant in HGG vs. control samples identifies important biomarkers including RPPH1 for monitoring disease and signaling progression The sequencing output was further queried with the DE plasma-EV sRNA determined to be significant in HGG vs. Control (above) using fold change analysis, to identify trends pre- and post-surgery as shown in the heat maps (Fig. 6 A,B). This type of analysis is somewhat limited by a high variability of residual disease, as shown (Fig. 5 B). Highlighted were a few sRNA enriched in HGG vs. control, which demonstrated significantly reduced expression after surgery such as miR-3648 ( p = 0.014) and SNORD42A ( p = 0.034). Few other sRNA showed a trend toward reduced expression after surgery, but did not reach significance, including MALAT1 ( p = 0.096), SNORD89 ( p = 0.14) (Fig. 6 A,B). RPPH1 , a known plasma EV biomarker in colorectal cancer [ 93 ], showed a significant reduction in expression post-surgery ( p = 0.006, Fig. 6 A). Given RPPH1 was significant in HGG vs. control and demonstrated reduced expression post-surgery, RPPH1 expression was queried in five patient plasma samples in our cohort, at the time of clinically defined progression (273 +/- 29 days post-surgery; n = 5) after standard treatment (Fig. 7 A). As anticipated, RRPH1 expression significantly increased in plasma-EV samples at clinically defined progression. Therefore, we characterize RPPH1 as an important plasma-EV biomarker that not only identifies HGG, but also serves as an indicator for surgical resection and HGG progression. RPPH1 is upregulated in HGG tissue and higher expression levels are associated with worse disease specific survival Given the strength of our data supporting RPPH1 as a novel biomarker in HGG and support from the literature for RPPH1 in other cancer subtypes, RPPH1 expression was queried in The Genotype-Tissue Expression (GTEx, normal brain tissue) and The Cancer Genome Atlas (TCGA) databases for HGG, using UCSC Xena [ 81 ]. See materials and methods for reclassification of astrocytomas based on WHO 2021 criteria. RPPH1 was present and identified as having greater expression in HGG tissue (n = 144) relative to normal brain tissue (n = 1141) (Fig. 7 B). Furthermore, Kaplan-Meier survival analysis showed that high RPPH1 tissue expression was associated with worse disease specific survival in IDH-WT HGG (Fig. 7 C). RPPH1 is enriched in both plasma-EVs, and tissues of IDH-WT HGG patients compared to controls and higher levels of expression is associated with a worse prognosis in these patients, further strengthening its importance as a novel HGG biomarker. Discussion The goal of non-invasive liquid biopsy is to identify biomarkers for HGG, which can predict the correct diagnosis of newly identified brain tumours, provide insight into relative survival or prognosis, be obtainable throughout the course of the disease to predict progression and/or response to adjunct therapies, and ultimately provide novel therapeutic targets. We profiled sRNA from Vn96-EVs isolated from HGG patient plasma sampled perioperatively and during post-surgical follow up, and demonstrated a unique, diagnostic sRNA signature for HGG. We analyzed the DE sRNA, mainly miRNA, using in silico tools to both validate and provide additional clinical relevance to our findings. Uniquely, we identified lncRNA RPPH1 as an important plasma EV biomarker for HGG, with expression levels informing on prognosis, extent of HGG resection, and tumour progression. Comparing across studies for small RNA biomarkers in HGG is complicated by numerous factors including but not limited to: 1) tissue versus liquid biopsy (blood), 2) differences in methods used for EV isolation, 3) library preparation kits that will enrich for specific (total vs small) RNA species. The functional significance of concordance or discordance of small RNA species between HGG tissue and HGG EVs is not yet fully understood as cells are known to selectively sort small RNA into EVs [ 94 ]. For example, through oncogenic transformation, HGG cells may retain pro-oncogenic small RNA while selectively sorting tumor suppressor small RNA into EVs for export In practice, for the sole purpose of plasma biomarkers of HGG disease, the end function of the small RNA are not required. Despite these differences, there are advantages to utilizing plasma for HGG biomarkers, as analyzing HGG tissue alone may exclude relevant biomarkers from the immune component of cancer that may also be important, and plasma is much easier to obtain in a longitudinal fashion. The fraction of blood utilized (serum versus plasma) can alter results as serum contains increased levels of platelet-derived particles which can influence profiling, hence our decision to utilize plasma [ 95 ]. Choices in library preparation kits also influence the abundance of small RNA species within transcriptomic analysis, for example the choice of oligo-dT reverse transcriptase priming would enrich for poly-adenylated mRNA over small RNA. Despite these challenges, similar biomarkers that are discovered across multiple studies should be considered strong candidates for clinical applications or therapeutic targets. Abnormal expression of miRNAs in cancer tissue and in tumour-associated EVs has been extensively studied in HGG and found to influence hallmark processes for growth and progression [ 64 – 66 ]. Due to the large volume of functional data on HGG and miRNA in the literature, bioinformatic analysis exists to functionally categorize miRNA data into localizations and disease processes. Utilizing miEAA analysis, our DE miRNAs were over-represented in categories related to localization in EVs, brain cancer, and functional categories of cancer hallmark pathways (miRWalk) involved in HGG proliferation, invasion, and angiogenesis. A significant number (26/34) of our DE miRNA have functional roles in HGG in the literature. The localization of a number of the same miRNA to EVs and their involvement in HGG pathways and disease supports that our sRNA EV biomarkers are indeed coming from the tumour microenvironment. Whether these are directly from HGG cells, or the surrounding supportive cells, will require further analysis. The miEAA analysis of the DE miRNA gives confidence to a true EV signal coming from the HGG tumor microenvironment. A review of the current HGG EV literature links our DE miRNAs to miRNA most commonly found in HGG EV studies. Recent reviews commonly cite the let-7 family, miR-21, miR-106, miR-130, miR-155, miR-185, miR-193, miR-210, miR-222, miR-451, miR-485 miR-486, and miR-574[ 96 , 97 ] as being found in multiple publications in relation HGG. Twelve of these miRNAs were identified in plasma EVs of HGG in this study, with three (let-7, miR-451, miR-485) being significantly overexpressed in HGG EVs compared to controls. Our analysis of tissue specificity demonstrates the significant enrichment of miR-485 to brain, again supporting the validity of this technique to determine biomarkers in HGG. Despite some discordance to the literature, acknowledging that there are clear technical differences, there are some miRNA biomarkers (let-7, mir 451 and mir-485) that are consistent across studies, and our data support these miRNA as liquid biopsy biomarkers or therapeutic targets in the future. Other DE species of sRNA sequenced when comparing HGG to controls included snoRNA, which function as guide RNAs for ribosomal RNA (rRNA) maturation and processing in the nucleolus [ 98 , 99 ], with additional roles in chromatin structure regulation, RNA splicing, and protein signaling [ 100 – 103 ]. Recent worked has highlighted the role of snoRNA in cancer by modulating anti-tumour immunity [ 104 ]. We identified 17 DE snoRNAs between HGG plasma-EVs and controls. Similar to other studies in cancer, the SNORD3 family, SNORD89 , and SNORD42A were found to be upregulated in HGG [ 48 , 105 – 107 ]. Relatively little is published directly about snoRNA plasma-EV expression in HGG, as most studies target miRNA or mRNA. SNORD44/47/76 have been identified as HGG tumour suppressors in tissue [ 108 – 110 ]. SNORD44 contained within the GAS5 transcript has been found to be decreased in this study, which has not been documented previously. However, GAS5 has shown a pro-tumourigenic role in HGG which raises the question if the whole transcript is relevant or just one or more of the snoRNA contained within GAS5[ 111 , 112 ] Interestingly, we show SNORD 32A to be significantly depleted in HGG plasma-EVs. Loss of SNORD32A in mammalian cells was found to be protective against oxidative stress [ 113 ], which would be critical in HGG, as it is known to be a highly hypoxic microenvironment. Further studies to screen snoRNA expression in HGG tissues and in vitro studies to manipulate expression of relevant snoRNA in HGG cell lines and EVs could improve our understanding of their functions in tumorigenesis. Importantly, our work and others demonstrate the peril of utilizing snoRNA as normalization factors in quantitative polymerase chain reaction in HGG and cancer. Comparing pre- and post-surgery plasma EV small RNA is complex and influenced by individual patient factors such as degree of resection, extent of residual tumour volume, post-surgical inflammation and kinetics of the tumour/EVs. Samples in this study were taken before surgery and two weeks post-surgery. Two weeks post-surgery was selected to allow EVs released during ultrasonic tumour resection and surgery to be depleted, and this timepoint is prior to induction to radiation and chemotherapy. However, whether this is the correct time to serve as a new baseline in long-term studies is not known. Although post-surgical changes in plasma small RNA EVs signatures were variable across samples, the data supports a thresholding effect of surgical resection on alterations in EV signatures. This occurs at 10 cm 3 where EV small RNA plasma signatures did not alter with greater residual volumes. This concept aligns with HGG survival data that suggests a similar thresholding effect of residual disease and HGG survival [ 114 , 115 ]. Interestingly, in a pediatric grade 4 brain tumour, medulloblastoma, 1.5 cm 3 is left on axial imaging is consistent with poor survival [ 116 ]. Researchers need to be aware of these potential thresholding effects when analyzing longitudinal changes in circulating biomarkers. We analyzed the changes in the specific DE miRNA found enriched in HGG vs controls after surgical debulking; although none reached significance with FDR, trends were observed (Fig. 4 A&B). miR-320d was depleted in HGG EVs presurgically and a trend toward increasing in EVs post-surgery. HGG tissue has demonstrated reduced expression of miR-320d in HGG in response to temozolomide (TMZ), suggesting a possible role for miR-320d in chemosensitivity [ 117 ]. In our comparison of pre- and post-surgical HGG plasma-EVs, we see an interesting trend of miR-320d upregulation post-surgery, which may signify a release of miR-320d inhibition upon sufficient resection of tumour and an overall anti-tumorigenic profile of plasma-EVs. Conversely, we see the opposite trend in pre- and post-surgical comparison of miR-3648, which was enriched in HGG plasma-EVs and showed a predominantly downward trend of expression following surgical resection. While miR-3648 has been characterized in numerous other cancers such as prostate, bladder, gastric, lung, and esophageal [ 118 – 122 ], it has not been previously found to be significant in HGG. In most cancers, miR-3648 has been shown to be a pro-oncogenic miRNA important in tumorigenesis and higher expression of miR-3648 in tissues have been linked with worse survival [ 123 ]. Y-RNA are a highly conserved and emerging class of non-coding small RNA that are biologically active found in EVs and involved in cancer [ 124 ]. Both RNY4 and RNY5 were elevated in HGG EVs compared to controls although interestingly these Y-RNA are processed differently. The 5’ (31nt) RNY5 fragment from EVs of cancer cells was found to trigger cell death preferentially in non-cancer cells, when compared to cancer cells. This suggested a role for the 5’ RNY5 fragment from cancer EVs in modulating the TME to selectively kill non-cancerous cells, while promoting tumour survival [ 91 ]. Previous work in HGG CSF demonstrated that both RNY4 and RNY5 were found as biomarkers in CSF and the exosomes of cultured glioma stem cells [ 47 ]. Given that plasma is easier to obtain, we can confirm both RNY4/5 to be biomarkers for HGG. Additive to this we can confirm further processing of these RNY5 into a smaller 5’fragment is the important biomarker. Our data demonstrates that it is this 5’ functional suicide fragment of RNY5 to be enriched in HGG plasma-EVs. This is in contrast to RNY4 , where enrichment of the full-length Y-RNA occurs in HGG plasma-EVs. Additionally, the finding of RNY4 , RNY5 and RPPH1 enriched and localized in HGG plasma-EVs could implicate a functional relationship between these non-coding RNA, which are all transcribed by RNA polymerase III (Pol III) [ 125 ]. RPPH1 , as the catalytic RNA component of Ribonuclease P (RNAse P), plays an important role in RNA cleavage and processing and it has been hypothesized by other authors to potentially have a role in cleavage of RNY5 , although studies are ongoing [ 126 , 127 ]. Further work demonstrating similar suicidality of the 5’ RNY5 fragment in non-HGG brain cells versus HGG is ongoing in our laboratory. In our study, RPPH1 was enriched in plasma-EVs in HGG samples relative to healthy controls and showed a drop in expression following tumour resection in all samples ( p = 0.006). RPPH1 levels increased again at time of clinical progression (as determined by the treating oncologist and MRI imaging). Our study is limited in determining the source of EVs containing RPPH1 in our plasma samples, albeit previous literature has suggested the majority of EVs in HGG are from the tumor cells of the TME [ 33 ]. However, we analyzed the TCGA tissue expression of RPPH1 in HGG using XENA [ 81 ]. We show that RPPH1 is significantly enriched in HGG tissue as compared to normal brain and that higher expression of RPPH1 in HGG is associated with a worse prognosis. This provides supporting evidence that RPPH1 expression is associated with the TME. This expression pattern of RPPH1 is similar to studies in colorectal cancer (CRC), where RPPH1 was found to be enriched in both plasma-derived exosomes from CRC patients and tumour tissues, and higher tissue expression was associated with worse overall survival [ 49 ]. In non-small cell lung cancer (NSCLC) and gastric cancer, higher RPPH1 expression was likewise associated with worse overall survival [ 50 , 128 ]. Interestingly, RPPH1 exists in alternate forms based on the pattern of splicing, including lnc - RPPH1 and circ - RPPH1 . Xue and colleagues analyzed the Gene Expression Omnibus (GEO) for circular RNA (circRNA) expression in GBM and demonstrated circ - RPPH1 was more highly expressed in HGG compared to normal tissue and predicted survival [ 129 ]. Our sequencing strategy would not allow for the sequencing of circRNA, however it warrants further investigation to explore the relationship between lnc- and circ - RPPH1 in HGG. RPPH1 has been shown to be involved in multiple cancer promoting pathways including: 1) as a miRNA sponge for miR-122 and miR-326, 2) promoting M2 polarization of tumor-associated macrophages, 3) WNT1 /Beta-catenin signaling, and 4) cleavage of precursor tRNA [ 49 , 50 , 125 , 130 , 131 ]. It will be important in future experiments to determine the functional role of RPPH1 in HGG. Like other liquid biopsy studies in HGG, we identify DE miRNA, snoRNA, sdRNA, Y-RNA, and lncRNA isolated from EVs that distinguish HGG from control samples and are involved in HGG pathogenesis. We are aware of a few studies [ 33 , 68 , 132 – 134 ] including ours that have sampled EVs from patients in a longitudinal manner (i.e., pre- and post-surgery, pre- or post-chemoradiotherapy, or routine surveillance), but to the best of our knowledge we are the first to profile the breadth of sRNA sequenced from EVs from matched pre-, post-surgery, and during clinical progression. Although other studies report on sRNA, the library preparation kit selection did not allow for sequencing of the entirety of sRNA. We also acknowledge that isolated EVs were not exclusive to those excreted by HGG tumour cells (as with 5-ALA based EV isolation; [ 34 ]) but reflect all plasma-EVs. We would argue that this may not necessarily be a disadvantage, as our sRNA signature would reflect the different sRNA cargoes of EVs derived from HGG microenvironment and not the HGG alone. With biomarkers we are more concerned with the message that a HGG exists and less concerned at this point on which cells send the message. Conclusion We show the potential for plasma EVs and their sRNA cargoes to be used as novel biomarkers and the feasibility for minimally invasive sampling of blood from HGG patients in a perioperative fashion. We have identified a sRNA signature from plasma-EVs that distinguishes HGG and may indicate adequate tumour resection. Our group is the first to show plasma EV RPPH1 is a meaningful biomarker of HGG from diagnosis, surgery to disease progression and thus could be utilized as part of a multi-modality disease surveillance. Given that RPPH1 is also highly expressed in HGG tissue and predicts survival, it is enticing to envision therapeutic strategies targeting RPPH1 (i.e., anti-sense RNA) in the future. Taken together our results demonstrate the validity of utilizing plasma EV sRNA as biomarkers of HGG and provide a rationale to prospectively follow these biomarkers in combination with imaging. Abbreviations 5-ALA 5-aminolevulinic acid ATCC american type culture collection CCAT2 colon cancer associated transcript 2 cDNA complementary DNA circ circular CRC colorectal cancer CSF cerebrospinal fluid DE differentially expressed EDTA Ethylenediaminetetraacetic acid EV extracellular vesicle FDR false discovery rate FGF fibroblast growth factor GBM glioblastoma GTEx the genotype-tissue expression HGG high grade glioma HIF1A-AS hypoxia-inducible factor 1A IDH-WT isocirtrate dehydrogenase wild type KM kaplan meier LNCARSR lncRNA regulator of akt signaling associated with HCC and RCC lncRNA long non-coding RNA MALAT1 metastasis associated lung adenocarcinoma transcript 1 MDS multidimensional scaling MGMT methylguanine methyltransferase miRNA micro RNA MRI magnetic resonance imaging mRNA messenger RNA MROCK1 MARCKS cis regulating lncRNA promoter of cytokines and inflammation NSCLC non-small cell lung cancer ORA over-representation analysis piRNA piwi-RNA RNY4 Ro60-associated Y4 RNY5 Ro60-Associated Y5 RPPH1 ribonuclease P component H1 RRID research resource identifier snoRNA small nucleolar RNA sRNA small RNA TBST tris-buffered saline with 0.1% Tween-20 TCGA the cancer genome atlas TME tumor microenvironment tRNA transfer RNA VEGF vascular endothelial growth factor WHO world health organization Declarations Ethics approval and consent to participate Ethics approval was obtained by the Research Ethics Board of Nova Scotia Health (REB#1023343). Adult patients (>18 years old) identified as having a suspected HGG who then underwent surgical resection or biopsy and were recruited from the Queen Elizabeth II Health Sciences Centre following written, informed consent. All methods were carried out in accordance with the Canadian Research Tri-Council policy on ethical conduct for research involving humans (https://ethics.gc.ca/eng/policy-politique_tcps2-eptc2_2018.html). Availability of data and materials The datasets generated and/or analysed during the current study are available in the NCBI Gene Expression Omnibus (GEO) repository under the accession number GSE269391. Competing Interests The authors report no conflicts to disclose. Funding This study was supported by grants from the Dalhousie University Faculty of Medicine, Department of Surgery, Dalhousie Medical Research Foundation (DMRF), Genome Atlantic, Research New Brunswick and the Beatrice Hunter Cancer Research Institute (BHCRI), with funds provided by the Terry Fox Research Institute’s Marathon of Hope Atlantic Cancer Consortium. Authors' contributions JH, GW, KA, MW, and LN were responsible for data generation, bioinformatics, manuscript draft writing and review. BH, RC, and SC performed small RNA sequencing, data generation and management, and manuscript review. AH prepared and managed the clinical research ethics board documents, assisted with patient data management and manuscript review. MM, MS, SC are major clinical collaborators and were responsible for patient management/follow up, MRI image analysis, biobanking and sample management. AW, JR conceptualized and designed the study, were responsible for financial acquisition, manuscript draft writing, review and finalization, and project supervision. All authors read and approved the final manuscript. Acknowledgements We would like to thank the patients affected by HGG and their families for agreeing to participate in this study. In particular, we would like to highlight the generous donations and fundraising efforts of Garry Beattie and Lori Duggan. We would like to thank the Dalhousie University Department of Anesthesia, Pain Management & Perioperative Medicine for their assistance with intraoperative sample collection. 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Hebb","email":"","orcid":"","institution":"Dalhousie University","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"L.O.","lastName":"Hebb","suffix":""},{"id":326954597,"identity":"7031fa30-8821-4603-ba56-7c706347831f","order_by":9,"name":"Mary V. MacNeil","email":"","orcid":"","institution":"Dalhousie University","correspondingAuthor":false,"prefix":"","firstName":"Mary","middleName":"V.","lastName":"MacNeil","suffix":""},{"id":326954598,"identity":"cda5844a-b783-481a-9d43-8b0b5935816c","order_by":10,"name":"Matthias H. Schmidt","email":"","orcid":"","institution":"Dalhousie University","correspondingAuthor":false,"prefix":"","firstName":"Matthias","middleName":"H.","lastName":"Schmidt","suffix":""},{"id":326954601,"identity":"c92e3295-105f-4b65-9a0d-0bf4bd47b4d2","order_by":11,"name":"Sidney E. Croul","email":"","orcid":"","institution":"Dalhousie University","correspondingAuthor":false,"prefix":"","firstName":"Sidney","middleName":"E.","lastName":"Croul","suffix":""},{"id":326954602,"identity":"49c14491-62c7-4999-a7e8-81ba63b439a1","order_by":12,"name":"Adrienne C. Weeks","email":"","orcid":"","institution":"Dalhousie University","correspondingAuthor":false,"prefix":"","firstName":"Adrienne","middleName":"C.","lastName":"Weeks","suffix":""},{"id":326954603,"identity":"4f5bf068-bece-49bf-9147-5547ec479727","order_by":13,"name":"Jeremy W. Roy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACxgYILQcX4SNWizFMQIKNWNsSG4jWwtze+/DRzbZ76f3tZw9++LjHro6NgfnhB7wO6zlubJzbVpw740xesuSMZ8lAW9iMJfBqmZHGJp3blpC7gSHHjJnnADNQCw8Dfi3zn7H/BmpJN+B/Y8b850A9SAvzD/y2sLExA7UkGEgAbWE4cBikhQ2/LT1pzNI55xIMZ9x4YyzZc+C4ZBszm5kFPi2G7ccYP+eUJcjz9+cYfvhxoJqfn7358Q28WhpAVqHEBTM+9UAgDyb/EFA1CkbBKBgFIxsAAGV6QHUcn+QQAAAAAElFTkSuQmCC","orcid":"","institution":"Atlantic Cancer Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Jeremy","middleName":"W.","lastName":"Roy","suffix":""}],"badges":[],"createdAt":"2024-07-05 18:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4693910/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4693910/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62153991,"identity":"2d1b4fce-73eb-4b4d-9098-f700f87dca51","added_by":"auto","created_at":"2024-08-09 20:55:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57298,"visible":true,"origin":"","legend":"\u003cp\u003eA) Western blot analysis demonstrating EVs isolated from pooled HGG-EVs utilizing Vn96 peptide affinity have canonical markers of EVs including Hsc70, CD63, FLOT1 and CD9. The non-EV marker GRP94 is detected at low levels. HEK 293T total cell lysate (HEK-CL) was used as a positive control. \u0026nbsp;Distribution of multi-annotated, uniquely annotated, and not annotated RNA reads upon sequencing control and HGG samples (B); Upset plot illustrating the distribution and frequency of uniquely annotated RNA sequences and multi-annotated sequences in plasma stratified by type of annotated small RNA. The columns are sorted according to a decreasing number of uniquely annotated RNA, double-annotated RNA, and multi-annotated RNA with three or more different annotations (C).\u003c/p\u003e","description":"","filename":"OnlineFig1new.png","url":"https://assets-eu.researchsquare.com/files/rs-4693910/v1/a9249928d33f7820d7cc0604.png"},{"id":62153992,"identity":"e43d9dd2-0af1-485f-b2c4-a820789b6216","added_by":"auto","created_at":"2024-08-09 20:55:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81618,"visible":true,"origin":"","legend":"\u003cp\u003eSmall RNA sequencing of plasma EV contents shows significant differences when comparing controls to HGG samples. Multi-dimensional scaling plot demonstrating the degree of similarity/dissimilarity amongst HGG samples and control samples (A). Volcano plots with red data points depicting differently expressed sRNA as labelled above the volcano plot with FDR \u0026lt; 0.05 (-log\u003csub\u003e10\u003c/sub\u003eFDR = 1.3) and log\u003csub\u003e2\u003c/sub\u003eFC \u0026gt; 1 or \u0026lt; -1 (B-H).\u003c/p\u003e","description":"","filename":"OnlineFig2new.png","url":"https://assets-eu.researchsquare.com/files/rs-4693910/v1/465be7a18b14c58f59987852.png"},{"id":62155143,"identity":"3e0183ee-3c2c-4b18-9027-cb5d93e8f8f8","added_by":"auto","created_at":"2024-08-09 21:03:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":144974,"visible":true,"origin":"","legend":"\u003cp\u003eTissue enrichment of DE miRNA. Normalized tissue expression level from TissueAtlas of 23 upregulated (A) and 11 downregulated (B) miRNA found comparing controls and HGG.\u003c/p\u003e","description":"","filename":"OnlineFig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4693910/v1/1f37573f19509ec8d469ad71.png"},{"id":62153997,"identity":"f509e66b-ac7c-4a1c-8fbb-69cfd36515b3","added_by":"auto","created_at":"2024-08-09 20:55:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":58927,"visible":true,"origin":"","legend":"\u003cp\u003eY-RNA, RNY4 and the 5’ fragment of RNY5 are significantly enriched in HGG plasma EVs compared to control samples. Utilizing IGV, we demonstrated the 31-nucleotide, 5’ effector fragment of RNY5, known to be involved in cancer pathogenesis is highly enriched in HGG (blue) compared to the 3’ fragment, whereas controls (green) show similar frequency of 3’ and 5’ fragments (A). Specifically, the 31-nucleotide (nt) 5’ RNY5 region is enriched in HGG compared to controls. The 3' region is similar between HGG and controls (B). There is no difference in the proportion of 5' and 3' fragments of RNY4 between HGG and controls (C). \u003cem\u003e*p\u003c/em\u003e \u0026lt; 0.05, ns = non-significant.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4693910/v1/17c1f56597f84b6efb50cecb.png"},{"id":62155140,"identity":"f976a544-d95a-44c8-b81e-8cfb9613695e","added_by":"auto","created_at":"2024-08-09 21:03:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":66010,"visible":true,"origin":"","legend":"\u003cp\u003eSmall RNA sequencing comparing pre- and post-surgery samples from high grade glioma patients. MDS plot demonstrating the degree of similarity/dissimilarity of sRNA profiles between pre-surgery and post-surgery samples (A). Tumour volume (mL) measured on T1-enhanced MRI imaging pre- and post-surgery for matched small RNA sequencing profiles (B). Interestingly, the tumours with the highest volume of residual had the least directional change suggesting sRNA signature may be sensitive to residual disease. Volcano plot with red data points depicting differently expressed RNA as labelled above the volcano plot with FDR \u0026lt; 0.05 (-log10FDR = 1.3) and log2FC \u0026gt; 1 or \u0026lt; -1 (C).\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4693910/v1/0eed59374917f9f117a0261d.png"},{"id":62153996,"identity":"fa85f954-e560-4bd7-9462-1c2d0db4e1f7","added_by":"auto","created_at":"2024-08-09 20:55:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":121938,"visible":true,"origin":"","legend":"\u003cp\u003eHeat maps showing the relative fold changes in expression comparing pre- and post-surgery samples with plasma-EV sRNA enriched or depleted in HGG. Blue colours denote increases in expression in post-surgery samples with darker hues reflecting larger, positive fold-changes. Yellow colours denote decreases in expression in post-surgery samples with darker hues reflecting larger fold-changes. Fold changes in small RNA expression pre- and post-surgery for those previously shown to be enriched in HGG (A) or depleted in HGG (B). *\u003cem\u003ep \u0026lt; 0.05.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"OnlineFig6new2.png","url":"https://assets-eu.researchsquare.com/files/rs-4693910/v1/be9a11f4930bb6035a718da6.png"},{"id":62155142,"identity":"4190b153-e895-41b8-b723-fda38bf3b891","added_by":"auto","created_at":"2024-08-09 21:03:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":67292,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRPPH1\u003c/em\u003e expression from HGG plasma EVs serves as a candidate biomarker. \u003cem\u003eRPPH1\u003c/em\u003eis DE in HGG (n = 10) relative to control plasma EVs (n = 4), shows significant reduction in expression following surgery (n = 6), and shows significant elevation in expression with clinically defined progression of HGG (n = 5) (A). \u003cem\u003eRPPH1\u003c/em\u003e expression is significantly greater in HGG tissue when compared to normal tissue and higher expression is associated with worsened prognosis in HGG patients. Violin plot comparing relative \u003cem\u003eRPPH1\u003c/em\u003e expression levels between normal brain tissue (n = 1141, GTEx database) and HGG (n = 144, TCGA database) as generated using Xena [81] (B). Kaplan-Meier curve demonstrating worse disease specific survival for the upper quartile vs. lower quartile of \u003cem\u003eRPPH1\u003c/em\u003eexpression as generated using Xena and querying TCGA low grade glioma and glioblastoma databases (see Materials and Methods) (C). *\u003cem\u003ep \u0026lt; 0.05; \u003c/em\u003e**\u003cem\u003ep \u0026lt; 0.01; \u003c/em\u003e****\u003cem\u003ep \u0026lt; 0.0001.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4693910/v1/5902d182cf19cd15fa901e14.png"},{"id":83485360,"identity":"ea7868c0-a7e9-40fb-a5da-1997ffc705e3","added_by":"auto","created_at":"2025-05-27 08:17:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2268488,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4693910/v1/10e3220e-dc13-4e4c-b451-dec7ef920905.pdf"},{"id":62155141,"identity":"05bf171c-e427-4bc5-b045-fe650e24109b","added_by":"auto","created_at":"2024-08-09 21:03:51","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":477906,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1miEAAAnalysisresults.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4693910/v1/87f6f6ffbedb040733f0b5de.xlsx"},{"id":62153994,"identity":"92098e2e-5989-4b87-a9db-d7e10682c45a","added_by":"auto","created_at":"2024-08-09 20:55:51","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1654558,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4693910/v1/1dfd19a07cf298c591842483.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Plasma extracellular vesicle sampling from high grade gliomas demonstrates a small RNA signature indicative of disease and identifies lncRNA RPPH1 as a high grade glioma biomarker.","fulltext":[{"header":"Background","content":"\u003cp\u003eHigh-grade gliomas (HGGs) are the most common primary malignant brain tumour, definitively diagnosed with invasive surgery [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The 2021 World Health Organization (WHO) classifies HGG as isocitrate dehydrogenase wild-type (\u003cem\u003eIDH\u003c/em\u003e-\u003cem\u003eWT\u003c/em\u003e), versus \u003cem\u003eIDH\u003c/em\u003e mutant tumours [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. \u003cem\u003eIDH\u003c/em\u003e mutation defines two subsets of tumour that differ in terms of patient demographic, genetic, and prognostic factors. The most common and more aggressive \u003cem\u003eIDH-WT\u003c/em\u003e HGG tumours, which includes diffuse Grade 3 astrocytomas and Grade 4 Glioblastoma Multiforme (GBM) were the focus of this study. \u003cem\u003eIDH-WT\u003c/em\u003e HGGs are universally fatal (median survival 14\u0026ndash;18 months) despite multimodal therapy (surgical resection, radiation, chemotherapy) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. HGG progression is the rule, as these tumours are proficient at adapting to a hostile microenvironment and co-opting surrounding normal brain cells (astrocytes and neurons), immune cells (e.g., microglia, monocytes, and macrophages), and endothelial cells to promote survival and progression [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent evidence suggests that several modalities of cell-to-cell communication between HGG cells and those of the tumour microenvironment (TME) enable pro-tumorigenic features [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Secretion of phospholipid membrane-bound extracellular vesicles (EVs) serve as an efficient means of bidirectional communication between HGG and cells of the TME to mediate autocrine/paracrine signaling [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For example, HGG cells release EVs containing pro-angiogenic proteins such as vascular endothelial growth factor (\u003cem\u003eVEGF\u003c/em\u003e) isoforms A and C, or fibroblast growth factor (\u003cem\u003eFGF\u003c/em\u003e) that target the surrounding endothelium and promote tumour vascularity [\u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Alternatively, EVs released by normal astrocytes containing tumour-suppressive cargo have been shown to inhibit growth of HGG [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEVs (ranging in size from 30nm to 1000nm) are secreted into all biofluids such as blood and cerebrospinal fluid (CSF) and are subdivided according to their size and subcellular origin (exosomes, microvesicles, and apoptotic bodies) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. While EVs are produced by almost all cell-types [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], malignant HGG cancer cells have been shown to increase production of EVs and their secretion into plasma [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Despite the blood-brain barrier, EVs derived from HGG tumours have been detected in the plasma, most notably in the well-designed experiment using surgically resected tissue and plasma collected from patients treated with 5-aminolevulinic acid (5-ALA) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Thus, the isolation of plasma EVs and elucidation of their cargo is an appealing non-invasive methodology (liquid biopsy) for informing on HGG. Numerous methods of EV isolation have been described in the literature to analyze various cancer pathologies [\u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The Vn96 peptide is a peptide affinity capture method developed for the capture of EVs in clinically relevant biofluids [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan additionalcitationids=\"CR39 CR40\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Numerous studies have shown captured Vn96-EVs from plasma contain canonical EV markers, and Vn96 has been utilized for the discovery of EV cargo-biomarkers in amyotrophic lateral sclerosis [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and cancers, such as prostate [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], lung [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], and pancreatic ductal adenocarcinoma [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNon-coding RNAs (such as miRNA, snoRNA, lncRNA, piRNA, and others) contained in EVs of HGG plasma and those of cancers outside of the brain, have already been shown to be effective forms of tumour-associated cell-to-cell communication [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan additionalcitationids=\"CR46 CR47\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. For example, the long non-coding RNA (lncRNA) ribonuclease P RNA component H1 (\u003cem\u003eRPPH1\u003c/em\u003e) has been shown to play an important role in multiple cancers (e.g., lung, colorectal cancer) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Moreover, \u003cem\u003eRPPH1\u003c/em\u003e contained in EVs isolated from colon cancer has been shown to regulate M2 polarization in macrophages of the TME, promoting proliferation and metastases of colon cancer cells [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In HGG patient plasma-EVs, \u003cem\u003eMALAT1\u003c/em\u003e was found to promote tumour proliferation and chemoresistance [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. HGG angiogenesis was mediated by EV-associated \u003cem\u003eCCAT2\u003c/em\u003e or \u003cem\u003eHIF1A-AS\u003c/em\u003e [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], and immune regulation of the HGG TME by EV \u003cem\u003eMROCKI\u003c/em\u003e or \u003cem\u003eLNCARSR\u003c/em\u003e [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The abnormal expression levels of various miRNA isolated from the EVs of HGG patient plasma have been found to mediate tumour aggressiveness and correlate with overall survival. Low levels of miR-485-3p were shown to correlate with significantly worse survival in HGG patients [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Angiogenesis was regulated by EV miR-1, miR-9, or miR-148a-3p [\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], and TME immune regulation by miR-451 and miR-21[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. EV miR-1238, miR-135b, or miR-151a were found to be critical in acquired treatment resistance [\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. These and other studies establish an ever-growing list implicating EVs and their sRNA cargo in many aspects of HGG tumorigenesis and cancer as a whole.\u003c/p\u003e \u003cp\u003eWe aim to demonstrate that HGG-associated plasma-EVs obtained with peptide affinity capture can serve as promising non-invasive biomarkers of disease with the potential to interrogate pro-tumour crosstalk [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan additionalcitationids=\"CR64 CR65 CR66 CR67\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. For the first time in HGG we have utilized peptide affinity capture of plasma-EVs to determine a sRNA signature that can inform on HGG in patient samples pre- and post-surgery. Our sRNA signature recapitulates previous data demonstrating the validity of our method and adds novel potential sRNA biomarkers. These data can be used to aid in the establishment of biomarkers of disease and potential therapeutic targets in the future.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003e\u003cem\u003ePatient and sample collection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Research Ethics Board of Nova Scotia Health (REB#1023343). Adult patients (\u0026gt;18 years old) identified as having a suspected HGG underwent surgical resection or biopsy were recruited from the Queen Elizabeth II Health Sciences Centre following written, informed consent. All surgeries were carried out under general anesthesia with standard monitoring of vitals, neuronavigation, and sterile surgical technique. Tumour resections were completed through a craniotomy overlying the tumour region. Biopsies were completed via a small craniotomy or burrhole with stereotactic guidance, at the discretion of the attending neurosurgeon. Whole blood samples were obtained longitudinally from just prior to the initiation of surgery (n = 10), 18 +/- 1 days post-surgery (n = 6), and at the time of clinically defined progression (n = 5). Only samples from patients with confirmed histopathological diagnosis of HGG (2021 WHO Grade III and IV tumours) were subsequently analyzed. Blood was collected in Vacutainer EDTA-tubes (BD). Control non-cancer plasma was obtained from Innovative Research Inc. (Novi, MI). Blood was processed within 2 hours of collection by centrifugation at 1,500xg for 15 min at 22\u0026deg;C. The plasma fraction was stored at -80\u0026deg;C. All methods were carried out in accordance with the Canadian Research Tri-Council policy on ethical conduct for research involving humans (https://ethics.gc.ca/eng/policy-politique_tcps2-eptc2_2018.html). Patients included is this study had to have pathology verified HGG (Grade 3 or 4) and be IDH-WT. \u0026nbsp;Patients with Grade 3 or 4 astrocytoma and IDH-mutations were excluded. Patients in whom long-term plasma collection was not feasible due to distance from primary care site were excluded. \u0026nbsp;Both male and female patients were recruited, however this study is underpowered to detect differences in sex variables and was not explored as part of this paper. As this was not a clinical trial, patients were not randomized, and investigators were not blinded to whether a sample was a control or HGG sample.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHGG tumour volume assessment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe volume of pre-surgical and post-surgical gadolinium-enhancing tumour tissue was measured on T1-weighted magnetic resonance imaging (MRI) scans, using semi-automated, intensity-based image segmentation. T1-hyperintense blood products seen on unenhanced T1-weighted images were digitally removed from gadolinium-enhanced T1-weighted images and were not included in the measurement of gadolinium-enhancing tumour tissue. Triplicate measurements were made by a single observer, and median values were recorded. Tumours without visible gadolinium enhancement were assigned a volume of zero.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVn96-mediated EV isolation and Protein or RNA extraction\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePlasma was thawed at room temperature and pre-cleared at 3,000 x \u003cem\u003eg\u0026nbsp;\u003c/em\u003efor 15 min. Peptide-affinity capture of EVs was performed following well established protocols\u0026nbsp;[40, 41, 43, 69]. EV-RNA was extracted with the miRVana miRNA isolation kit (Invitrogen) following manufacturer\u0026rsquo;s protocol for total RNA. EV-Protein was precipitated from the organic fraction using acetone and solubilized with 8 M urea, 0.2% SDS and 1 M Tris-HCl, pH 6.8 similar to\u0026nbsp;[70, 71]. Total RNA quantity and profile was assessed using Fragment Analyzer 5200 (Agilent Technologies Inc.).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWestern blot analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEV-Protein samples (25 \u0026micro;g) were divided into two aliquots of equal volume and one sample prepared under reducing conditions with 10% \u0026beta;-mercaptoethanol and the other under non-reducing conditions. Samples were loaded onto Any KD Mini-Protean TGX Stain-Free Protein gels (Bio-Rad) and transferred to 0.45 \u0026micro;m polyvinylidene fluoride membranes. Membranes were blocked with 5% (w/v) skim milk in tris-buffered saline with 0.1 % Tween-20 (TBST). Primary antibodies were prepared in TBST with 5% skim milk: Hsc-70 (1:200, Santa Cruz Biotechnology Cat# SC-7298, RRID:AB_627761), CD63 (1:200, Santa Cruz Biotechnology Cat# SC-5275,\u0026nbsp;RRID:AB_627877), FLOT1 (1:1000, Cell Signaling Technology, Cat #18634, RRID:AB_2773040), CD9 (1:200, Santa Cruz Biotechnology Cat# SC-59140, RRID: AB_1120766), and GRP94 (1:1000, New England Biolabs, Cat#2104S, RRID:AB_823506). Anti-mouse IgG (1:5,000, Jackson ImmunoResearch, Cat#115-035-003,\u0026nbsp;RRID:AB_10015289), and anti-rabbit IgG (1:5,000, Jackson ImmunoResearch, Cat#111-035-003,\u0026nbsp;RRID:AB_2313567) secondary antibodies were used as appropriate and conjugated to horse radish peroxidase. HEK 293T cells were obtained from ATCC (Cat# CRL-3216). HEK 293T whole cell lysate protein was used as a positive control, cell lines are tested monthly for Mycoplasma and only negative cell lines were utilized.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003esRNA sequencing and bioinformatics\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNext-generation sequencing was performed on EV-RNA (3-10 ng) using the NextFlex Small RNA sequencing kit v3 (Perkin Elmer) following manufacturers\u0026rsquo; protocol. cDNA libraries were quantified on iSeq prior to loading on a NovaSeq 6000 (Illumina).Raw reads from sRNAseq were converted to fastq files and the adapter sequences (TGGAATTCTCGGGTGCCAAGG) were removed using TrimGalore (v0.6.5;\u0026nbsp;\u003ca href=\"https://github.com/FelixKrueger/TrimGalore\"\u003ehttps://github.com/FelixKrueger/TrimGalore\u003c/a\u003e). Alignment was performed using STAR (v2.7.0f;\u0026nbsp;[72],\u0026nbsp;RRID:SCR_004463) and the GRCh38 human genome with the parameters \u0026ldquo;\u0026mdash;outFilterScoreMinOverLread 0 \u0026nbsp; --outFilterMatchNmin 16 \u0026nbsp; --outFilterMatchNminOverLread 0 \u0026nbsp; --outFilterMismatchNoverLmax 0.05 \u0026nbsp; --alignIntronMax 1 \u0026nbsp;--alignEndsType EndToEnd\u0026rdquo;. The resulting .bam files were passed through a counting method by chromosome location using the Bioconductor package derfinder (v1.18.9;\u0026nbsp;RRID:SCR_006442; [73]). Finally, the chromosome positions with counts were annotated using multiple databases such as Gencode (v38;\u0026nbsp;[74]), pirnaDB (v1.7.6;\u0026nbsp;[75]), MINT tRNA fragment database (v2.0;\u0026nbsp;[76]), and Mirbase (v21;\u0026nbsp;[77]). R statistical environment (v4.2.2) was used to calculate the variance between the normalized expressed chromosome regions read counts with trimmed mean of M-values (TMM) normalization method and differential expression analysis was performed using the Bioconductor package edgeR (v3.18.1;\u0026nbsp;RRID:SCR_006442;\u0026nbsp;[78]). R was used to build the following complimentary figures: upset plots with upset function from \u0026lsquo;upsetR\u0026rsquo;; MDS plots were built with plotMDS function from \u0026lsquo;edgeR\u0026rsquo; library and ggplot from \u0026lsquo;ggplot2\u0026rsquo; library; and the volcano plots were built using ggplot from \u0026lsquo;ggplot2\u0026rsquo;library. A false discovery rate (FDR Benjamini-Hochberg adjustment) of less than or equal to 0.05 and a fold change of greater than 1 or less than -1 were considered significant for further analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunctional analysis and tissue specificity of miRNA\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFunctional analysis of the differentially expressed (DE) mature miRNAs between control and HGG patient samples was facilitated by the miRNA enrichment analysis and annotation tool (miEAA 2.0)\u0026nbsp;[79]. Over-representation analysis (ORA) was performed with the default statistical parameters (FDR Benjamini-Hochberg adjustment and significance level 0.05). Selected databases included \u0026ldquo;Localization (RNALocate)\u0026rdquo;, \u0026ldquo;Diseases (MNDR)\u0026rdquo;, and \u0026ldquo;Pathways (miRWalk)\u0026rdquo;. Results of the analysis showing significantly over- or under-represented categories were exported to Excel (Microsoft) spreadsheets and ordered by the number of observed miRNAs. \u0026nbsp;Tissue enrichment analysis for DE miRNA was facilitated by miRNAtissueAtlas2\u0026nbsp;[80]. For each DE miRNA (n = 34) the median reads per million was downloaded for the tissue panel, including brain, bone, lung, liver, lymph, bowel, muscle, heart and kidney. Median reads per million for each miRNA was divided by the maximum expression for each specific tissue and data was visualized by a heatmap.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAnalysis of sRNA expression (RPPH1) in tissue and assessment of prognostic value\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe expression of sRNA in tissue were queried from cancer and normal tissue repositories from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) databases accessed through the UCSC Xena browser (http://xenabrowser.net) [81]. To determine the expression \u003cem\u003eRPPH1\u003c/em\u003e in normal tissue as compared with HGG tissue, the TCGA Target GTEx database was filtered to select from only normal brain tissue (GTEx) and \u003cem\u003eIDH-WT\u003c/em\u003e HGG tissue (TCGA). \u003cem\u003eRPPH1\u003c/em\u003e expression was profiled with respect to tissue type and the raw data was displayed as a violin plot. Welch\u0026rsquo;s t-test was used to determine statistical significance. Kaplan-Meier (KM) survival analysis was performed using the TCGA low grade glioma and glioblastoma database. Through Xena, grade 3 and 4 \u003cem\u003eIDH-WT\u003c/em\u003e HGG were selected. Furthermore, as the TCGA database used an antiquated classification scheme (denoting all grade 2 \u003cem\u003eIDH-WT\u0026nbsp;\u003c/em\u003etumours as low-grade gliomas), those grade 2 samples with any known driver mutations/amplifications for HGG\u0026nbsp;[3, 82\u0026ndash;84]\u0026nbsp;were included. Eight grade 2 samples with amplifications of \u003cem\u003eEGFR\u003c/em\u003e, \u003cem\u003ePDGFRA/B\u003c/em\u003e, \u003cem\u003eMDM2/4\u003c/em\u003e, or mutations in \u003cem\u003ePTEN\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eH3F3A\u003c/em\u003e, \u003cem\u003eTERT\u003c/em\u003e, \u003cem\u003eCDKN2A/B\u003c/em\u003e, \u003cem\u003eRB1\u003c/em\u003e, \u003cem\u003eNF1\u003c/em\u003e, \u003cem\u003ePIK3CA\u003c/em\u003e, \u003cem\u003eEGFR\u003c/em\u003e, \u003cem\u003ePDGFRA/B\u003c/em\u003e, or \u003cem\u003ePDGFA/B\u003c/em\u003e\u003cem\u003e/C/D\u003c/em\u003e were reclassified as HGG. KM curve was generated to compare HGG patients with high \u003cem\u003eRPPH1\u003c/em\u003e expression to those with low \u003cem\u003eRPPH1\u003c/em\u003e expression. Statistical significance was displayed as a \u003cem\u003ep-\u003c/em\u003evalue utilizing the log-rank test. \u003cem\u003eP-\u003c/em\u003evalue \u0026lt; 0.05 was considered significant. Analysis of \u003cem\u003eRPPH1\u0026nbsp;\u003c/em\u003eexpression between pre-surgery, post-surgery and progression was calculated using ANOVA with multiple comparisons (p\u0026lt;0.05).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatient cohort characteristics\u003c/h2\u003e \u003cp\u003eAge and sex of control patient\u0026rsquo;s plasma samples, along with baseline age, sex, and select tumour characteristics of ten HGG patients are shown (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean age of patients with tumours at the time of surgery was 65 years, and eight patients were male. All tumours were \u003cem\u003eIDH-WT\u003c/em\u003e HGG. Half of the tumours were found to have methylated \u003cem\u003eMGMT\u003c/em\u003e promoter regions and seven were predominantly localized to the temporal lobe (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImportant patient and tumour characteristics of high grade glioma (HGG) patients and commercial, control plasma samples. *Anaplastic astrocytoma patient was \u003cem\u003eIDH\u003c/em\u003e wild type.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHigh Grade Glioma IDH WT\u0026nbsp;(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControls (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at diagnosis, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SEM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (57\u0026ndash;74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, no.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMales (% of total)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (75%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemales (% of total)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistopathology, no.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlioblastoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnaplastic astrocytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMGMT status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethylated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmethylated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDH mutation status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWildtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumour region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrontal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemporal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParietal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccipital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003esRNAs are differentially expressed between control and HGG plasma EVs\u003c/h2\u003e \u003cp\u003eFollowing the International Society of Extracellular vesicle (ISEV) guidelines, we confirmed the capture of EVs by Vn96 in HGG plasma, by detecting canonical EV-protein markers using Western blot analysis [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. For this we pooled HGG EV-protein in order to conserve these precious samples. As demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA we detected canonical EV-protein markers Hsc-70, CD63, CD9 and FLOT1 in the pooled HGG EV sample and very low amounts of the non-EV protein GRP94. This demonstrates the capture of EVs from plasma of HGG patients, similar to previously reported [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan additionalcitationids=\"CR87\" citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Sequencing and bioinformatic analysis of sRNA isolated from plasma EVs of controls and those from HGG patients, identified 4147 regions mapped to the human genome. The vast majority of the regions were uniquely annotated regions, although multi-annotated and non-annotated regions were also identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The distribution and frequency of uniquely annotated and multi-annotated regions stratified by the type of annotated sRNA is shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Most uniquely annotated regions were mRNA fragments, followed by lncRNA fragments, and a smaller proportion consisted of uniquely annotated tRNA, miRNA, and snoRNA. Most multi-annotated regions showed shared sequences with mRNA fragments, followed by lncRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Considering uniquely annotated and multi-annotated miRNA, there were 542 miRNAs sequenced. Multi-dimensional scaling (MDS) plot shows control samples appearing to cluster distinctly from the HGG samples, suggesting dissimilarity between these two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Differential expression (DE) analysis was performed and identified 789 regions, that passed statistical parameters, namely, false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (-log\u003csub\u003e10\u003c/sub\u003eFDR\u0026thinsp;=\u0026thinsp;1.3) and a fold change (FC) of log\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;1 or \u0026lt; -1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). To better understand the complexity of DE regions, volcano plots were generated for the most relevant annotated sRNA species including miRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), snoRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), lncRNA full length/fragments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), mRNA fragments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF), tRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG), and for regions that have no known annotation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eFunctional analysis of differentially expressed miRNA reveal associations with HGG and pathways involved in HGG pathogenesis\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOf the 528 miRNAs sequenced, 34 mature miRNAs were found to be significantly DE between control and HGG samples (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Considering the direction of fold change, 22 mature miRNAs were found to be enriched in HGG, while 12 were found to be depleted in HGG (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferentially expressed mature miRNA between control and HGG. Mature miRNAs are sorted by increasing FDR.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnriched in HGG plasma EVs-Mature ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLog\u003csub\u003e2\u003c/sub\u003e Fold change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFalse Discovery Rate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-451a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-200a-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-4511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-3648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-6803-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-218-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-223-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-6741-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-197-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-122-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-504-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-4755-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-143-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-1-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-328-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-4755-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-148b-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-16-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-485-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-let-7i-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-let-7i-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-7-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDepleted in HGG plasma EVs-Mature ID\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-4536-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-31-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-6866-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-223-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-320b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-320a-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-320d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-320c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-6881-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-887-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUtilizing miEAA 2.0, over-representation analysis (ORA) indicated significant over-representation of the 34 mature miRNAs in categories and subcategories associated with HGGs, cancer, and EVs (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, ORA revealed over-representation of the 34 mature miRNAs in categories associated with hallmarks of HGG pathogenesis including angiogenesis, proliferation/invasion (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), and their relevant signaling pathways. For the full ORA output see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. In order to potentially understand the tissue origin of our DE miRNA, we utilized Tissue Atlas 2.0 (TissueAtlas (uni-saarland.de)). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea number of our DE miRNA have enriched expression in brain (miR-485-3p, miR-504-5p, miR-6881-3p and miR-184) and bone (miR-4511, miR-223-5p, miR-4755-5p, miR-6741-3p, and miR-4755-3p).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSignificantly enriched categories related to localization and disease obtained utilizing ORA (miEAA 2.0) [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e] for 34 DE miRNAs when comparing control to high grade glioma patients. Importantly, this demonstrates with functional annotation and pathway analysis of our DE miRNAs, added confidence in the sRNA signature (especially the miRNA contribution), which are over-represented in subcategories for EVs and HGG.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubcategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnrichment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFalse Discovery Rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eObserved miRNA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocalization (RNALocate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMicrovesicle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eover-represented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e(hsa-miR-451a; hsa-miR-200a-3p; hsa-miR-4511; hsa-miR-3648; hsa-miR-218-5p; hsa-miR-223-3p; hsa-miR-223-5p; hsa-miR-197-3p; hsa-miR-122-5p; hsa-miR-504-5p; hsa-miR-4755-3p; hsa-miR-143-3p; hsa-miR-1-3p; hsa-miR-328-3p; hsa-miR-148b-3p; hsa-miR-16-5p; hsa-miR-485-3p; hsa-let-7i-5p; hsa-let-7i-3p; hsa-miR-7-5p; hsa-miR-31-5p; hsa-miR-320b; hsa-miR-320a-3p; hsa-miR-320d; hsa-miR-320c; hsa-miR-887-3p; hsa-miR-184; hsa-miR-484)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiseases (MNDR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eover-represented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e(hsa-miR-451a; hsa-miR-200a-3p; hsa-miR-4511; hsa-miR-218-5p; hsa-miR-223-3p; hsa-miR-223-5p; hsa-miR-197-3p; hsa-miR-122-5p; hsa-miR-504-5p; hsa-miR-4755-3p; hsa-miR-143-3p; hsa-miR-1-3p; hsa-miR-328-3p; hsa-miR-4755-5p; hsa-miR-148b-3p; hsa-miR-16-5p; hsa-miR-485-3p; hsa-let-7i-3p; hsa-miR-7-5p; hsa-miR-31-5p; hsa-miR-320b; hsa-miR-320a-3p; hsa-miR-320d; hsa-miR-320c; hsa-miR-887-3p; hsa-miR-184; hsa-miR-484)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiseases (MNDR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlioblastoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eover-represented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e(hsa-miR-451a; hsa-miR-200a-3p; hsa-miR-4511; hsa-miR-218-5p; hsa-miR-223-3p; hsa-miR-223-5p; hsa-miR-197-3p; hsa-miR-122-5p; hsa-miR-504-5p; hsa-miR-143-3p; hsa-miR-1-3p; hsa-miR-328-3p; hsa-miR-148b-3p; hsa-miR-16-5p; hsa-miR-485-3p; hsa-let-7i-5p; hsa-let-7i-3p; hsa-miR-7-5p; hsa-miR-31-5p; hsa-miR-320b; hsa-miR-320a-3p; hsa-miR-320d; hsa-miR-320c; hsa-miR-887-3p; hsa-miR-184; hsa-miR-484)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiseases (MNDR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalignant glioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eover-represented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e(hsa-miR-451a; hsa-miR-200a-3p; hsa-miR-218-5p; hsa-miR-223-3p; hsa-miR-223-5p; hsa-miR-197-3p; hsa-miR-122-5p; hsa-miR-504-5p; hsa-miR-143-3p; hsa-miR-1-3p; hsa-miR-328-3p; hsa-miR-148b-3p; hsa-miR-16-5p; hsa-miR-485-3p; hsa-let-7i-5p; hsa-miR-7-5p; hsa-miR-31-5p; hsa-miR-320b; hsa-miR-320a-3p; hsa-miR-320c; hsa-miR-184; hsa-miR-484)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocalization (RNALocate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExosome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eover-represented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e(hsa-miR-451a; hsa-miR-200a-3p; hsa-miR-218-5p; hsa-miR-223-3p; hsa-miR-197-3p; hsa-miR-122-5p; hsa-miR-143-3p; hsa-miR-328-3p; hsa-miR-148b-3p; hsa-miR-16-5p; hsa-let-7i-5p; hsa-miR-7-5p; hsa-miR-31-5p; hsa-miR-320a-3p; hsa-miR-887-3p; hsa-miR-484)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSignificantly enriched categories related to angiogenesis obtained via ORA (miEAA 2.0) [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e] for 34 DE miRNAs when comparing control to high grade glioma patients. This demonstrates the potential role for EV-mediated miRNA transport to contribute to cell to cell communication in the TME, by regulating new blood vessel formation or proliferation and invasion.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubcategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnrichment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFalse Discovery Rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eObserved miRNA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAngiogenesis\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathways (miRWalk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP00005 Angiogenesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eover-represented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathways (miRWalk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP00021 FGF signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eover-represented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathways (miRWalk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP00056 VEGF signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eover-represented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathways (miRWalk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP04393 Ras Pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eover-represented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathways (miRWalk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP00057 Wnt signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eover-represented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eProliferation and Invasion\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathways (miRWalk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP00018 EGF receptor signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eover-represented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathways (miRWalk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP00034 Integrin signalling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eover-represented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathways (miRWalk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04350 TGF beta signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eover-represented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathways (miRWalk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP00048 PI3 kinase pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eover-represented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathways (miRWalk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP00012 Cadherin signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eover-represented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathways (miRWalk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWP422 MAPK Cascade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eover-represented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSnoRNA, lncRNA, and Y-RNA are differentially expressed between control and HGG plasma-EVs\u003c/h2\u003e \u003cp\u003eAlso detected were DE sRNA species other than miRNA, for which pathway analysis was unavailable. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, seven snoRNA were found enriched in HGG whereas nine were found depleted. For example, members of the \u003cem\u003eSNORD3\u003c/em\u003e family (\u003cem\u003eSNORD3A\u003c/em\u003e, \u003cem\u003e3B\u003c/em\u003e, \u003cem\u003e3C\u003c/em\u003e, \u003cem\u003e3D\u003c/em\u003e), \u003cem\u003eSNORD14B\u003c/em\u003e, \u003cem\u003eSNORD42A\u003c/em\u003e, and \u003cem\u003eSNORD89\u003c/em\u003e were found to be enriched in HGG. In addition, 54 enriched and 103 depleted annotated regions to lncRNA full length/fragments were detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Since the library preparation kit used was designed for small RNA most of the regions annotated for lncRNA (and similarly mRNA) were only fragments of the entire transcript. This is similar to the output previously reported by others [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. However, in some cases, where the full-length functional lncRNA transcript is short, a large portion of the transcript was detected and statistically significant differences in these lncRNA were sequenced. For example, for lncRNAs \u003cem\u003eMALAT1\u003c/em\u003e, and \u003cem\u003eRPPH1\u003c/em\u003e, large portions of the transcript were sequenced (~\u0026thinsp;40% and ~\u0026thinsp;100% respectively) and these were enriched in HGG.\u003c/p\u003e \u003cp\u003eIn certain cases, particularly in cancer, DE of fragments of sRNA may be clinically important. Like other studies [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e], where cell-death effector fragments of \u003cem\u003eRNY5\u003c/em\u003e and tumour-suppressive fragments of \u003cem\u003eRNY4\u003c/em\u003e were detected in EVs from cancer cells, we detected enrichment in the 5\u0026rsquo; (31 nt) effector region of \u003cem\u003eRNY5\u003c/em\u003e in HGG (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). \u003cem\u003eRNY4\u003c/em\u003e, was also found to be enriched in HGG plasma-EVs, but unlike \u003cem\u003eRNY5\u003c/em\u003e, there was no significant difference in the proportions of \u003cem\u003eRNY4\u003c/em\u003e fragments seen between HGG and controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Examples of additional DE lncRNA and Y-RNA are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferentially expressed snoRNA, lncRNA, and Y-RNA between control and high grade glioma. snoRNA, lncRNA, and Y-RNA are sorted by increasing FDR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEnriched In HGG plasma EVs-ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLog\u003csub\u003e2\u003c/sub\u003e Fold change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFalse Discovery Rate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnoRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNORD3B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.00x10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNORD3C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.00x10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNORD3D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0x10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNORD3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.22x10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNORD14B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNORD42A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNORD89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elncRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMALAT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRPPH1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRP1-283E3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY-RNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRNY5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRNY4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDepleted in HGG plasma EVs-ID\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esnoRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNORA62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNORD64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNORD67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNORA5C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNORD111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNORA79B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNORD11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNORD44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNORD49A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNORD32A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elncRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRP11-306O13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLINC02067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTD-2651B20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTD-2651B20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHELLPAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRP1-236J16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNORAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTAGAP-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPR176-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCYTOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSmall RNA profiling shows differences between pre- and post-surgery HGG plasma-EVs that may signify volume of residual tumour remaining post-surgery\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWe sought to see changes in sRNA profiles following surgery for HGG resection and interpret any differences in pre- and post-surgery profiles, in relation to the volume of tumour remaining post-surgery. Pre- and post-surgery plasma-EV samples were compared from six matched HGG patients. We first performed MDS to visualize global changes in sRNA signatures between pre- and post-surgery samples. As visualized on a MDS plot, sequencing of sRNA isolated from plasma-EVs revealed heterogenous changes in expression levels of sRNA when comparing matched pre- and post-surgery samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Although this analysis was complicated by the low sample size and various extents of resection, there appeared to be substantial, but heterogenous changes in four of the six HGG paired samples. Two pairs of samples did not show a marked change and were found clustered together as highlighted in the red circle. Interestingly, these two patients showed a high volume of post-surgical residual tumour (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Small RNA profiling of plasma-EVs appeared to be influenced by the extent of tumour remaining after surgery, with larger tumour volumes remaining post-surgery being associated with minimal changes in sRNA profiles, when comparing matched pre- and post-surgery samples. Differential expression analysis, with the same statistical parameters described previously, revealed only 23 regions DE between pre- and post-surgery samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). For a list of DE sRNA in pre- and post-surgery plasma-EV comparison, see Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ePost-surgical trends in sRNA identified as significant in HGG vs. control samples identifies important biomarkers including RPPH1 for monitoring disease and signaling progression\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe sequencing output was further queried with the DE plasma-EV sRNA determined to be significant in HGG vs. Control (above) using fold change analysis, to identify trends pre- and post-surgery as shown in the heat maps (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA,B). This type of analysis is somewhat limited by a high variability of residual disease, as shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Highlighted were a few sRNA enriched in HGG vs. control, which demonstrated significantly reduced expression after surgery such as miR-3648 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014) and \u003cem\u003eSNORD42A\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034). Few other sRNA showed a trend toward reduced expression after surgery, but did not reach significance, including \u003cem\u003eMALAT1\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.096), \u003cem\u003eSNORD89\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA,B). \u003cem\u003eRPPH1\u003c/em\u003e, a known plasma EV biomarker in colorectal cancer [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e], showed a significant reduction in expression post-surgery (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Given \u003cem\u003eRPPH1\u003c/em\u003e was significant in HGG vs. control and demonstrated reduced expression post-surgery, \u003cem\u003eRPPH1\u003c/em\u003e expression was queried in five patient plasma samples in our cohort, at the time of clinically defined progression (273 +/- 29 days post-surgery; n\u0026thinsp;=\u0026thinsp;5) after standard treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). As anticipated, \u003cem\u003eRRPH1\u003c/em\u003e expression significantly increased in plasma-EV samples at clinically defined progression. Therefore, we characterize \u003cem\u003eRPPH1\u003c/em\u003e as an important plasma-EV biomarker that not only identifies HGG, but also serves as an indicator for surgical resection and HGG progression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eRPPH1 is upregulated in HGG tissue and higher expression levels are associated with worse disease specific survival\u003c/em\u003e \u003c/p\u003e \u003cp\u003eGiven the strength of our data supporting \u003cem\u003eRPPH1\u003c/em\u003e as a novel biomarker in HGG and support from the literature for \u003cem\u003eRPPH1\u003c/em\u003e in other cancer subtypes, \u003cem\u003eRPPH1\u003c/em\u003e expression was queried in The Genotype-Tissue Expression (GTEx, normal brain tissue) and The Cancer Genome Atlas (TCGA) databases for HGG, using UCSC Xena [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. See materials and methods for reclassification of astrocytomas based on WHO 2021 criteria. \u003cem\u003eRPPH1\u003c/em\u003e was present and identified as having greater expression in HGG tissue (n\u0026thinsp;=\u0026thinsp;144) relative to normal brain tissue (n\u0026thinsp;=\u0026thinsp;1141) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Furthermore, Kaplan-Meier survival analysis showed that high \u003cem\u003eRPPH1\u003c/em\u003e tissue expression was associated with worse disease specific survival in \u003cem\u003eIDH-WT\u003c/em\u003e HGG (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). \u003cem\u003eRPPH1\u003c/em\u003e is enriched in both plasma-EVs, and tissues of \u003cem\u003eIDH-WT\u003c/em\u003e HGG patients compared to controls and higher levels of expression is associated with a worse prognosis in these patients, further strengthening its importance as a novel HGG biomarker.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe goal of non-invasive liquid biopsy is to identify biomarkers for HGG, which can predict the correct diagnosis of newly identified brain tumours, provide insight into relative survival or prognosis, be obtainable throughout the course of the disease to predict progression and/or response to adjunct therapies, and ultimately provide novel therapeutic targets. We profiled sRNA from Vn96-EVs isolated from HGG patient plasma sampled perioperatively and during post-surgical follow up, and demonstrated a unique, diagnostic sRNA signature for HGG. We analyzed the DE sRNA, mainly miRNA, using \u003cem\u003ein silico\u003c/em\u003e tools to both validate and provide additional clinical relevance to our findings. Uniquely, we identified lncRNA \u003cem\u003eRPPH1\u003c/em\u003e as an important plasma EV biomarker for HGG, with expression levels informing on prognosis, extent of HGG resection, and tumour progression.\u003c/p\u003e \u003cp\u003eComparing across studies for small RNA biomarkers in HGG is complicated by numerous factors including but not limited to: 1) tissue versus liquid biopsy (blood), 2) differences in methods used for EV isolation, 3) library preparation kits that will enrich for specific (total vs small) RNA species. The functional significance of concordance or discordance of small RNA species between HGG tissue and HGG EVs is not yet fully understood as cells are known to selectively sort small RNA into EVs [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. For example, through oncogenic transformation, HGG cells may retain pro-oncogenic small RNA while selectively sorting tumor suppressor small RNA into EVs for export In practice, for the sole purpose of plasma biomarkers of HGG disease, the end function of the small RNA are not required. Despite these differences, there are advantages to utilizing plasma for HGG biomarkers, as analyzing HGG tissue alone may exclude relevant biomarkers from the immune component of cancer that may also be important, and plasma is much easier to obtain in a longitudinal fashion. The fraction of blood utilized (serum versus plasma) can alter results as serum contains increased levels of platelet-derived particles which can influence profiling, hence our decision to utilize plasma [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. Choices in library preparation kits also influence the abundance of small RNA species within transcriptomic analysis, for example the choice of oligo-dT reverse transcriptase priming would enrich for poly-adenylated mRNA over small RNA. Despite these challenges, similar biomarkers that are discovered across multiple studies should be considered strong candidates for clinical applications or therapeutic targets.\u003c/p\u003e \u003cp\u003eAbnormal expression of miRNAs in cancer tissue and in tumour-associated EVs has been extensively studied in HGG and found to influence hallmark processes for growth and progression [\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Due to the large volume of functional data on HGG and miRNA in the literature, bioinformatic analysis exists to functionally categorize miRNA data into localizations and disease processes. Utilizing miEAA analysis, our DE miRNAs were over-represented in categories related to localization in EVs, brain cancer, and functional categories of cancer hallmark pathways (miRWalk) involved in HGG proliferation, invasion, and angiogenesis. A significant number (26/34) of our DE miRNA have functional roles in HGG in the literature. The localization of a number of the same miRNA to EVs and their involvement in HGG pathways and disease supports that our sRNA EV biomarkers are indeed coming from the tumour microenvironment. Whether these are directly from HGG cells, or the surrounding supportive cells, will require further analysis. The miEAA analysis of the DE miRNA gives confidence to a true EV signal coming from the HGG tumor microenvironment. A review of the current HGG EV literature links our DE miRNAs to miRNA most commonly found in HGG EV studies. Recent reviews commonly cite the let-7 family, miR-21, miR-106, miR-130, miR-155, miR-185, miR-193, miR-210, miR-222, miR-451, miR-485 miR-486, and miR-574[\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e] as being found in multiple publications in relation HGG. Twelve of these miRNAs were identified in plasma EVs of HGG in this study, with three (let-7, miR-451, miR-485) being significantly overexpressed in HGG EVs compared to controls. Our analysis of tissue specificity demonstrates the significant enrichment of miR-485 to brain, again supporting the validity of this technique to determine biomarkers in HGG. Despite some discordance to the literature, acknowledging that there are clear technical differences, there are some miRNA biomarkers (let-7, mir 451 and mir-485) that are consistent across studies, and our data support these miRNA as liquid biopsy biomarkers or therapeutic targets in the future.\u003c/p\u003e \u003cp\u003eOther DE species of sRNA sequenced when comparing HGG to controls included snoRNA, which function as guide RNAs for ribosomal RNA (rRNA) maturation and processing in the nucleolus [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e], with additional roles in chromatin structure regulation, RNA splicing, and protein signaling [\u003cspan additionalcitationids=\"CR101 CR102\" citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. Recent worked has highlighted the role of snoRNA in cancer by modulating anti-tumour immunity [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]. We identified 17 DE snoRNAs between HGG plasma-EVs and controls. Similar to other studies in cancer, the \u003cem\u003eSNORD3\u003c/em\u003e family, \u003cem\u003eSNORD89\u003c/em\u003e, and \u003cem\u003eSNORD42A were\u003c/em\u003e found to be upregulated in HGG [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan additionalcitationids=\"CR106\" citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e]. Relatively little is published directly about snoRNA plasma-EV expression in HGG, as most studies target miRNA or mRNA. \u003cem\u003eSNORD44/47/76\u003c/em\u003e have been identified as HGG tumour suppressors in tissue [\u003cspan additionalcitationids=\"CR109\" citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. SNORD44 contained within the GAS5 transcript has been found to be decreased in this study, which has not been documented previously. However, GAS5 has shown a pro-tumourigenic role in HGG which raises the question if the whole transcript is relevant or just one or more of the snoRNA contained within GAS5[\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e] Interestingly, we show SNORD\u003cem\u003e32A\u003c/em\u003e to be significantly depleted in HGG plasma-EVs. Loss of \u003cem\u003eSNORD32A\u003c/em\u003e in mammalian cells was found to be protective against oxidative stress [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e], which would be critical in HGG, as it is known to be a highly hypoxic microenvironment. Further studies to screen snoRNA expression in HGG tissues and \u003cem\u003ein vitro\u003c/em\u003e studies to manipulate expression of relevant snoRNA in HGG cell lines and EVs could improve our understanding of their functions in tumorigenesis. Importantly, our work and others demonstrate the peril of utilizing snoRNA as normalization factors in quantitative polymerase chain reaction in HGG and cancer.\u003c/p\u003e \u003cp\u003eComparing pre- and post-surgery plasma EV small RNA is complex and influenced by individual patient factors such as degree of resection, extent of residual tumour volume, post-surgical inflammation and kinetics of the tumour/EVs. Samples in this study were taken before surgery and two weeks post-surgery. Two weeks post-surgery was selected to allow EVs released during ultrasonic tumour resection and surgery to be depleted, and this timepoint is prior to induction to radiation and chemotherapy. However, whether this is the correct time to serve as a new baseline in long-term studies is not known. Although post-surgical changes in plasma small RNA EVs signatures were variable across samples, the data supports a thresholding effect of surgical resection on alterations in EV signatures. This occurs at 10 cm\u003csup\u003e3\u003c/sup\u003e where EV small RNA plasma signatures did not alter with greater residual volumes. This concept aligns with HGG survival data that suggests a similar thresholding effect of residual disease and HGG survival [\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e]. Interestingly, in a pediatric grade 4 brain tumour, medulloblastoma, 1.5 cm\u003csup\u003e3\u003c/sup\u003e is left on axial imaging is consistent with poor survival [\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e]. Researchers need to be aware of these potential thresholding effects when analyzing longitudinal changes in circulating biomarkers.\u003c/p\u003e \u003cp\u003eWe analyzed the changes in the specific DE miRNA found enriched in HGG vs controls after surgical debulking; although none reached significance with FDR, trends were observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026amp;B). miR-320d was depleted in HGG EVs presurgically and a trend toward increasing in EVs post-surgery. HGG tissue has demonstrated reduced expression of miR-320d in HGG in response to temozolomide (TMZ), suggesting a possible role for miR-320d in chemosensitivity [\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e]. In our comparison of pre- and post-surgical HGG plasma-EVs, we see an interesting trend of miR-320d upregulation post-surgery, which may signify a release of miR-320d inhibition upon sufficient resection of tumour and an overall anti-tumorigenic profile of plasma-EVs. Conversely, we see the opposite trend in pre- and post-surgical comparison of miR-3648, which was enriched in HGG plasma-EVs and showed a predominantly downward trend of expression following surgical resection. While miR-3648 has been characterized in numerous other cancers such as prostate, bladder, gastric, lung, and esophageal [\u003cspan additionalcitationids=\"CR119 CR120 CR121\" citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e], it has not been previously found to be significant in HGG. In most cancers, miR-3648 has been shown to be a pro-oncogenic miRNA important in tumorigenesis and higher expression of miR-3648 in tissues have been linked with worse survival [\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eY-RNA are a highly conserved and emerging class of non-coding small RNA that are biologically active found in EVs and involved in cancer [\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e]. Both RNY4 and RNY5 were elevated in HGG EVs compared to controls although interestingly these Y-RNA are processed differently. The 5\u0026rsquo; (31nt) \u003cem\u003eRNY5\u003c/em\u003e fragment from EVs of cancer cells was found to trigger cell death preferentially in non-cancer cells, when compared to cancer cells. This suggested a role for the 5\u0026rsquo; \u003cem\u003eRNY5\u003c/em\u003e fragment from cancer EVs in modulating the TME to selectively kill non-cancerous cells, while promoting tumour survival [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. Previous work in HGG CSF demonstrated that both \u003cem\u003eRNY4\u003c/em\u003e and \u003cem\u003eRNY5\u003c/em\u003e were found as biomarkers in CSF and the exosomes of cultured glioma stem cells [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Given that plasma is easier to obtain, we can confirm both RNY4/5 to be biomarkers for HGG. Additive to this we can confirm further processing of these \u003cem\u003eRNY5\u003c/em\u003e into a smaller 5\u0026rsquo;fragment is the important biomarker. Our data demonstrates that it is this 5\u0026rsquo; functional suicide fragment of \u003cem\u003eRNY5\u003c/em\u003e to be enriched in HGG plasma-EVs. This is in contrast to \u003cem\u003eRNY4\u003c/em\u003e, where enrichment of the full-length Y-RNA occurs in HGG plasma-EVs. Additionally, the finding of \u003cem\u003eRNY4\u003c/em\u003e, \u003cem\u003eRNY5\u003c/em\u003e and \u003cem\u003eRPPH1\u003c/em\u003e enriched and localized in HGG plasma-EVs could implicate a functional relationship between these non-coding RNA, which are all transcribed by RNA polymerase III (Pol III) [\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e]. \u003cem\u003eRPPH1\u003c/em\u003e, as the catalytic RNA component of Ribonuclease P (RNAse P), plays an important role in RNA cleavage and processing and it has been hypothesized by other authors to potentially have a role in cleavage of \u003cem\u003eRNY5\u003c/em\u003e, although studies are ongoing [\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e, \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e]. Further work demonstrating similar suicidality of the 5\u0026rsquo; RNY5 fragment in non-HGG brain cells versus HGG is ongoing in our laboratory.\u003c/p\u003e \u003cp\u003eIn our study, \u003cem\u003eRPPH1\u003c/em\u003e was enriched in plasma-EVs in HGG samples relative to healthy controls and showed a drop in expression following tumour resection in all samples (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). \u003cem\u003eRPPH1\u003c/em\u003e levels increased again at time of clinical progression (as determined by the treating oncologist and MRI imaging). Our study is limited in determining the source of EVs containing \u003cem\u003eRPPH1\u003c/em\u003e in our plasma samples, albeit previous literature has suggested the majority of EVs in HGG are from the tumor cells of the TME [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, we analyzed the TCGA tissue expression of \u003cem\u003eRPPH1\u003c/em\u003e in HGG using XENA [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. We show that \u003cem\u003eRPPH1\u003c/em\u003e is significantly enriched in HGG tissue as compared to normal brain and that higher expression of \u003cem\u003eRPPH1\u003c/em\u003e in HGG is associated with a worse prognosis. This provides supporting evidence that \u003cem\u003eRPPH1\u003c/em\u003e expression is associated with the TME. This expression pattern of \u003cem\u003eRPPH1\u003c/em\u003e is similar to studies in colorectal cancer (CRC), where \u003cem\u003eRPPH1\u003c/em\u003e was found to be enriched in both plasma-derived exosomes from CRC patients and tumour tissues, and higher tissue expression was associated with worse overall survival [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In non-small cell lung cancer (NSCLC) and gastric cancer, higher \u003cem\u003eRPPH1\u003c/em\u003e expression was likewise associated with worse overall survival [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e]. Interestingly, \u003cem\u003eRPPH1\u003c/em\u003e exists in alternate forms based on the pattern of splicing, including \u003cem\u003elnc\u003c/em\u003e-\u003cem\u003eRPPH1\u003c/em\u003e and \u003cem\u003ecirc\u003c/em\u003e-\u003cem\u003eRPPH1\u003c/em\u003e. Xue and colleagues analyzed the Gene Expression Omnibus (GEO) for circular RNA (circRNA) expression in GBM and demonstrated \u003cem\u003ecirc\u003c/em\u003e-\u003cem\u003eRPPH1\u003c/em\u003e was more highly expressed in HGG compared to normal tissue and predicted survival [\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e]. Our sequencing strategy would not allow for the sequencing of circRNA, however it warrants further investigation to explore the relationship between lnc- and \u003cem\u003ecirc\u003c/em\u003e-\u003cem\u003eRPPH1\u003c/em\u003e in HGG. \u003cem\u003eRPPH1\u003c/em\u003e has been shown to be involved in multiple cancer promoting pathways including: 1) as a miRNA sponge for miR-122 and miR-326, 2) promoting M2 polarization of tumor-associated macrophages, 3) \u003cem\u003eWNT1\u003c/em\u003e/Beta-catenin signaling, and 4) cleavage of precursor tRNA [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e, \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e, \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e]. It will be important in future experiments to determine the functional role of \u003cem\u003eRPPH1\u003c/em\u003e in HGG.\u003c/p\u003e \u003cp\u003eLike other liquid biopsy studies in HGG, we identify DE miRNA, snoRNA, sdRNA, Y-RNA, and lncRNA isolated from EVs that distinguish HGG from control samples and are involved in HGG pathogenesis. We are aware of a few studies [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan additionalcitationids=\"CR133\" citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e] including ours that have sampled EVs from patients in a longitudinal manner (i.e., pre- and post-surgery, pre- or post-chemoradiotherapy, or routine surveillance), but to the best of our knowledge we are the first to profile the breadth of sRNA sequenced from EVs from matched pre-, post-surgery, and during clinical progression. Although other studies report on sRNA, the library preparation kit selection did not allow for sequencing of the entirety of sRNA. We also acknowledge that isolated EVs were not exclusive to those excreted by HGG tumour cells (as with 5-ALA based EV isolation; [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]) but reflect all plasma-EVs. We would argue that this may not necessarily be a disadvantage, as our sRNA signature would reflect the different sRNA cargoes of EVs derived from HGG microenvironment and not the HGG alone. With biomarkers we are more concerned with the message that a HGG exists and less concerned at this point on which cells send the message.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe show the potential for plasma EVs and their sRNA cargoes to be used as novel biomarkers and the feasibility for minimally invasive sampling of blood from HGG patients in a perioperative fashion. We have identified a sRNA signature from plasma-EVs that distinguishes HGG and may indicate adequate tumour resection. Our group is the first to show plasma EV \u003cem\u003eRPPH1\u003c/em\u003e is a meaningful biomarker of HGG from diagnosis, surgery to disease progression and thus could be utilized as part of a multi-modality disease surveillance. Given that \u003cem\u003eRPPH1\u003c/em\u003e is also highly expressed in HGG tissue and predicts survival, it is enticing to envision therapeutic strategies targeting \u003cem\u003eRPPH1\u003c/em\u003e (i.e., anti-sense RNA) in the future.\u003c/p\u003e \u003cp\u003eTaken together our results demonstrate the validity of utilizing plasma EV sRNA as biomarkers of HGG and provide a rationale to prospectively follow these biomarkers in combination with imaging.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e5-ALA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e5-aminolevulinic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eATCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eamerican type culture collection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCAT2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecolon cancer associated transcript 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecDNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecomplementary DNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecirc\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecircular\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecolorectal cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCSF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecerebrospinal fluid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edifferentially expressed\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEDTA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEthylenediaminetetraacetic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eextracellular vesicle\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efalse discovery rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFGF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efibroblast growth factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGBM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eglioblastoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGTEx\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe genotype-tissue expression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh grade glioma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHIF1A-AS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehypoxia-inducible factor 1A\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIDH-WT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eisocirtrate dehydrogenase wild type\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ekaplan meier\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLNCARSR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elncRNA regulator of akt signaling associated with HCC and RCC\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003elncRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elong non-coding RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMALAT1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emetastasis associated lung adenocarcinoma transcript 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emultidimensional scaling\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMGMT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emethylguanine methyltransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emiRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emicro RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emagnetic resonance imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emessenger RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMROCK1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMARCKS cis regulating lncRNA promoter of cytokines and inflammation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNSCLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-small cell lung cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eORA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eover-representation analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003epiRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epiwi-RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRNY4\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRo60-associated Y4\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRNY5\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRo60-Associated Y5\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRPPH1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eribonuclease P component H1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRRID\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eresearch resource identifier\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003esnoRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esmall nucleolar RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003esRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esmall RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTBST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etris-buffered saline with 0.1% Tween-20\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe cancer genome atlas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTME\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor microenvironment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003etRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etransfer RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVEGF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003evascular endothelial growth factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eworld health organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval was obtained by the Research Ethics Board of Nova Scotia Health (REB#1023343). Adult patients (\u0026gt;18 years old) identified as having a suspected HGG who then underwent surgical resection or biopsy and were recruited from the Queen Elizabeth II Health Sciences Centre following written, informed consent. All methods were carried out in accordance with the Canadian Research Tri-Council policy on ethical conduct for research involving humans (https://ethics.gc.ca/eng/policy-politique_tcps2-eptc2_2018.html).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the NCBI Gene Expression Omnibus (GEO) repository under the accession number GSE269391.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no conflicts to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the Dalhousie University Faculty of Medicine, Department of Surgery, Dalhousie Medical Research Foundation (DMRF), Genome Atlantic, Research New Brunswick and the Beatrice Hunter Cancer Research Institute (BHCRI), with funds provided by the Terry Fox Research Institute\u0026rsquo;s Marathon of Hope Atlantic Cancer Consortium.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJH, GW, KA, MW, and LN were responsible for data generation, bioinformatics, manuscript draft writing and review. BH, RC, and SC performed small RNA sequencing, data generation and management, and manuscript review. AH prepared and managed the clinical research ethics board documents, assisted with patient data management and manuscript review.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMM, MS, SC are major clinical collaborators and were responsible for patient management/follow up, MRI image analysis, biobanking and sample management.\u003c/p\u003e\n\u003cp\u003eAW, JR conceptualized and designed the study, were responsible for financial acquisition, manuscript draft writing, review and finalization, and project supervision. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the patients affected by HGG and their families for agreeing to participate in this study. In particular, we would like to highlight the generous donations and fundraising efforts of Garry Beattie and Lori Duggan. We would like to thank the Dalhousie University Department of Anesthesia, Pain Management \u0026amp; Perioperative Medicine for their assistance with intraoperative sample collection. We would also like to the neurosurgeons of the Dalhousie University Division of Neurosurgery, including Dr. David Clarke, Dr. Simon Walling, Dr. Stephen Lownie, Dr. Daniel McNeely, Dr. Gwynedd Pickett, Dr. Sean Christie, Dr. Sean Barry, Dr. Lutz Weise, and Dr. Jacob Alant for their assistance with patient recruitment and intraoperative sample collection.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDeAngelis LM (2001) Brain Tumors. N Engl J Med 344:114\u0026ndash;123\u003c/li\u003e\n \u003cli\u003eOstrom QT, Gittleman H, Stetson L, Virk SM, Barnholtz-Sloan JS (2015) Epidemiology of gliomas. 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Clinical Cancer Research OF1\u0026ndash;OF13\u003cu\u003e\u003c/u\u003e\u003cu\u003e\u003cbr\u003e\u003c/u\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"High-Grade Glioma, Extracellular vesicles, Small RNA sequencing, Biomarkers, Lnc RPPH1","lastPublishedDoi":"10.21203/rs.3.rs-4693910/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4693910/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eHigh grade gliomas (HGGs) and cells of the tumour microenvironment (TME) secrete extracellular vesicles(EVs) into the plasma that contain genetic and protein cargo, which function in paracrine signaling. Isolation of these EVs and their cargo from plasma could lead to a simplistic tool that can inform on diagnosis and disease course of HGG.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn the present study, plasma EVs were captured utilizing a peptide affinity method (Vn96 peptide) from HGG patients and normal controls followed by next generation sequencing (NovaSeq6000) to define a small RNA (sRNA) signature unique to HGG.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOver 750 differentially expressed sRNA (miRNA, snoRNA, lncRNA, tRNA, mRNA fragments and non-annotated regions) were identified between HGG and controls. MiEAA 2.0 pathway analysis of the miRNA in the sRNA signature revealed miRNA highly enriched in both EV and HGG pathways demonstrating the validity of results in capturing a signal from the TME. Also revealed were several novel HGG plasma EV sRNA biomarkers including lncRNA \u003cem\u003eRPPH1\u003c/em\u003e (Ribonuclease P Component H1), RNY4 (Ro60-Associated Y4) and RNY5 (Ro60-Associated Y5). Furthermore, in paired longitudinal patient plasma sampling, \u003cem\u003eRPPH1\u003c/em\u003e informed on surgical resection (decreased on resection) and importantly, \u003cem\u003eRPPH1\u003c/em\u003e increased again on clinically defined progression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe present study supports the role of plasma EV sRNA sampling (and particularly \u003cem\u003eRPPH1\u003c/em\u003e) as part of a multi-pronged approach to HGG diagnosis and disease course surveillance.\u003c/p\u003e","manuscriptTitle":"Plasma extracellular vesicle sampling from high grade gliomas demonstrates a small RNA signature indicative of disease and identifies lncRNA RPPH1 as a high grade glioma biomarker.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 20:55:46","doi":"10.21203/rs.3.rs-4693910/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5d355688-2b79-4013-81d6-cde7f0d1d02a","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-27T08:09:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-09 20:55:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4693910","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4693910","identity":"rs-4693910","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}