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A sncRNA signature found in microvesicles of GBM patient serum serves as a diagnostic marker Lorea Manterola, Elizabeth Guruceaga, Jaime Gállego Pérez-Larraya, Marisol Gonzalez-Huarriz, Patricia Jauregui, Victor Segura, Nicolás Sampron, Cristina Barrena, Irune Ruiz, Sonia Tejada, Ricardo Diez-Valle, Amaia Agirre, Angel Ayuso, Javier Rodriguez, Alvaro Gonzalez, Ander Matheu, Teresa Tuñon, Idoya Zazpe, Enric Xipell, Adolfo Lopez de Munain, Jesús García-Foncillas, Sophie Paris, Jean Yves Delattre, Marta M. Alonso Affiliations of Authors Lorea Manterola, Nicolas Sampron, Cristina Barrena, Irune Ruiz, Ander Matheu and Adolfo López de Munain, Instituto Biodonostia and Hospital Universitario Donostia, San Sebastian, Spain, Elizabeth Guruceaga, Marisol Gonzalez-Huarriz, Patricia Jauregui, Victor Segura, Sonia Tejada, Ricardo Diez-Valle, Jaime Gallego, Javier Rodriguez, Alvaro Gonzalez, Enric Xipell, Marta M. Alonso, Center for Applied Medical Research (CIMA) and Clínica Universidad de Navarra (CUN), University of Navarra, Pamplona, Spain; Amaia Agirre, Polymat, University of the Basque Country, San Sebastian, Spain; Angel Ayuso, IMMA-CIOCC, Fundación Hospital de Madrid, Madrid, Spain; Jesús García-Foncillas, Fundación Jimenez-Díaz, Madrid, Spain; Jaime Gallego, Sophie Paris, Jean Yves Delattre, Pitie-Salpetriere, Paris, France; Teresa Tuñon and Idoya Zazpe, Complejo Hospitalario de Navarra, Pamplona, Spain. This work was supported by European Union (Marie Curie IRG270459 to MMA); Spanish Ministry of Health (ISCII PI10/00399 to MMA), Spanish Ministry of Science and Innovation (Ramón y Cajal contract RYC-2009-05571 to MMA); Diputación Foral de Guipuzcoa DFG 09/003 to LM) and Spanish Ministry of Health (Sara Borrell contractCD06/0275 to LM) Correspondence to: Marta M Alonso PhD, Department of Medical Oncology, Clínica Universidad de Navarra, CIMA Building, Avd/Pio XII, 55 Pamplona (Spain); Phone: +34-948194700-1017; e-mail: mmalonso@unav.es

sncRNA signature in MVs isolated from GBM serum

Presented at the EANO10thAnnual Conference (Marseille, 6-9 September 2012) and the SNO 17th 2012 Annual Conference (Washington, 15-18 November 2012).

The authors indicated no potential conflicts of interest

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ABSTRACT

Purpose. Glioblastomas (GBMs) are the most frequent malignant brain tumors in adults, and

their prognosis remains dismal despite intensive research and therapeutic advances. Diagnostic

biomarkers would be clinically meaningful to allow for early detection of the tumor and for

those cases in which surgery is contraindicated or biopsies result inconclusive. Recent findings

show that GBM cells release microvesicles that contain a select subset of cellular proteins and

RNA. We undertook this study to assess whether we could find a miRNA signature in

microvesicles isolated from serum of GBM patients that could serve as a diagnostic tool.

Patients and Methods. To control disease heterogeneity we used patients with newly

diagnosed GBM. In the discovery stage, TLDA PCR based-arrays followed by individual

quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) were used to test the

differences in the miRNAs expression level of serum microvesicles between 25 GBM patients

and 25 healthy controls paired by age and sex. The detected serum microvesicles non-coding

RNAs were then validated in 50 GBM patients. In addition we assess the expression of this

non-coding RNA signature in 9 brain metastasis and 15 gastrointestinal tumors.

Results. We found that the expression levels of one sncRNA (RNU6-1) and 2 miRNAs (miR-

320 and miR-574-3p) were significantly associated with a GBM diagnostic. In addition,

RNU6-1 was consistently an independent predictor of a GBM diagnostic.

Conclusions. Altogether our results uncovered a sncRNA signature in MVs isolated from

GBM patient serum that could be used as a differential diagnostic biomarker for patients with

GBM.

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INTRODUCTION

Glioblastoma multiforme (GBM) is the most frequent malignant primary brain tumor in adults.

Despite recent advances in treatment, which mainly consists on surgery and combined radio-

chemotherapy 1, the prognosis of GBM patients remains dismal with median overall survival

times of less than 15 months. Although neuroimaging may suggest its diagnosis, other brain

lesions such as abscesses, metastases and other types of tumors might share radiological

features with GBM 2. Hence, histological examination of the tumor tissue obtained by surgery

is currently mandatory for definite diagnosis. Therefore, the development of minimally

invasive tests for the detection and monitoring of GBM would be clinically meaningful,

especially in those cases in which biopsies result inconclusive or surgery is contraindicated

provide a significant advance in the diagnosis of this devastating disease. Although

conventional strategies for blood-based biomarker discovery have been shown promising, the

development of clinically validated cancer detection markers remains an unmet challenge for

many common cancers 3, especially GBM.

Exosomes are a type of microvesicles (MVs) formed by endosomal membrane invagination,

that later fuse to the plasmatic membrane and are released out of the cell 4. They are size and

shape homogeneous (30–100 nm in diameter), and their content and biological function depend

on the cell of origin 5. They are released under physiological conditions by various types of

cells (review in 6), and interestingly, they have been found circulating in peripheral blood 7 8.

Their release is increased in tumor cells 9,10 and it has been shown that cancer patients have

higher amount of circulating exosomes compared with healthy controls 11. Recently, two

different groups described the release of microvesicles (MVs), and more specifically

exosomes, from GBM cells 12,13. Importantly, these MVs isolated from the serum of GBM

patients have been pointed as potential biomarkers reservoirs 12,13. MVs are enriched in

miRNA, small non-coding RNAs (sncRNA) that play important roles in oncogenesis 14.

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Several studies have shown that signatures of miRNAs expression differ between normal and

tumor tissues, and also vary among tumor types 15. Notably, miRNA expression signatures

have been proposed as prognostic markers 16. Several reports have shown that human plasma

and serum contains a large amount of stable miRNAs, and that unique expression patterns of

serum miRNAs under specific disease conditions imply a great potential of serum miRNA

profiling as the fingerprint of the disease 17.

In this study we hypothesized that a miRNA signature in serum MVs might be used as a non-

invasive diagnostic tool in GBM patients. To address this hypothesis, we screened serum MVs

miRNAs by using PCR based arrays followed by an extensively study that used individual

qRT-PCR assays in two independent cohorts of GBM patients.

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PATIENTS AND METHODS

Patients

For this study serum from 75 patients with histologically proven GBM, according to the World

Health Organization classification, were used. Serum samples were collected at the Pitié-

Salprètriere Hospital (Paris, France), Clínica Universidad de Navarra (CUN; Pamplona, Spain),

Hospital Universitario Donostia (San Sebastian, Spain), Basque Research Biobank-O+Ehun,

SanSebastian (SanSebastian, Spain), Complejo Hospitalario de Navarra (Pamplona, Spain) and

the Tumor Biobank of the Servicio Navarro de Salud-Osasunbidea (Pamplona, Spain). The

control group consisted of 55 healthy donors from the Basque Research Biobank-O+Ehun and

subjects recruited at CUN. The study protocol was approved by the Institutional Review Board,

and all the participants signed the informed consent form approved by the respective

Institutional Review Boards or Ethical Committees.

For the initial training group, serum samples from 25 GBM patients (14 men and 11 women;

media age 59.6 ± 10,6 years, range 30–75) and 25 healthy controls paired for sex and age were

included (14 men and 11 women; media age 60 ± 10,9 years, range 45–78) (Appendix Table

A1, online only). For the validation study, serum from additional 50 GBM patients (30 men

and 20 women; media age 61 ± 12.9 years, range 17–79) and 30 healthy control (14 men and

16 women; media age 54.3 ± 11.8 years, range 27–70) were analyzed (Appendix Table A2,

online only). Additionally, 9 serum samples from patients with brain metastases and 15

gastrointestinal cancers were also collected (Appendix Table A3 and A4, online only).

Microvesicle Isolation from Human Subjects.

Microvesicles (MVs) were isolated from the serum using Exoquick precipitation solution

according to the manufacturer’s recommendations (System Biosciences, Mountain View, CA,

USA).

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miRNA profiling by quantitative real-time PCR

miRNA expression was analyzed using the Applied Biosystems TaqMan® Human microRNA

A Array v2.1 (TaqMan Low Density Array, TLDA; Applied Biosystems, Foster City, CA) to

profile 381 mature miRNAs by qRT-PCR. Raw Ct values were calculated using the SDS

software version 2.4 using an automatic baseline settings and a threshold of 0.05.

Specific miRNAs were further validated by qRT-PCR using individual TaqMan miRNA

assays. All reactions were performed using a 7900HT RT-PCR instrument (Applied

Biosystems) and triplicate samples were used throughout.

TLDA data analysis

TLDA data were analyzed with R/Bioconductor 18. The analysis consisted in a filtering process

in order to eliminate miRNAs that were not detected in the experiment. We considered Ct

scores greater than 35 non-specific 19,20. Therefore, miRNAs that had a raw Ct value greater

than 35 in more than 50% of the patients or controls samples were excluded. Using these

filtering criteria we considered for statistical analysis only 48 miRNAs.

LIMMA (Linear Models for Microarray Data) was used to find out the differentially expressed

miRNAs 21. A False Discovery Rate (FDR) of 0.05 22 and an expression change (−ΔCt) of at

least 2 Ct was established as selection criteria. miRNA expression normalization was

performed following two different approaches: using the RNU48 as endogenous control and

using median normalization analysis 23. Both normalization methods identified almost the same

miRNAs de-regulated with a significant FDR supporting each other. We decided to use

RNU48 as endogenous control for further validation analysis because the feasibility to

implement one gene versus the median of the whole PCR array in the following validation

studies.

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Validation of TLDAs and the independent validation group

The normalization for the Ct values of the TLDA validation (individual miR qRT-PCR) and

the independent validation group of samples was performed using RNU48 as endogenous

control. Fold change was calculated as the difference of the mean normalized expression values

between GBM patients and controls (−ΔCt = mean (ΔCt GBM patients) – – mean (ΔCt

controls)). A t-test analysis was performed with R/Bioconductor 18 and miRNAs were

considered differentially expressed using a threshold of p<0.01.

Biomarker discovery using a classifier approach

A machine learning algorithm based on logistic regression was applied to classify patients and

identify the optimal separating miRNAs between GBM patients and healthy controls 24. The

performance of the classifiers was evaluated using Receiver Operator Characteristic (ROC)

analysis 25.

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RESULTS

Characterization of microvesicles isolated from GBM patient serum

The aim of this study was to determine whether a specific miRNAs profile found in MVs

isolated from the serum of GBM patients could serve as biomarker of the disease. First, we

isolated MVs from serum of GBM patients and healthy controls using an adsorption method

(Exoquick TM). Exoquick is a compound of undisclosed composition that has been proposed

on the market to precipitate by a single step exosomes from small volumes of serum or cell

culture supernatant. This method is less time consuming compared with the traditional method

of gradient ultra-centrifugation, and therefore it would be easier to implement in a clinical

setting.

In order to confirm their nature, we characterized the MVs that had been obtained from GBM

patients’ serum and healthy controls using Exoquick. We assessed the morphology and size

using transmission electron microscopy and dynamic light scattering. These analyses showed

two set of membrane like structures: one with a diameter ranging from 12.86 to 23.81 nm and

other set of MVs with sizes ranging from 157.9 to 210.8 nm (Fig 1A and B). We further

evaluated the expression of several MVs/exosomes markers by western blot in total serum and

in MVs isolated from healthy controls and GBM patients. We found that the expression of

exosomes related markers such as CD9, Lamp1, TSG 101 and Alix 5 was enriched in the MVs

of both controls and patients when compared with total serum (Fig 1C). Importantly, the

endoplasmic reticulum marker calnexin was not detected in these samples suggesting that our

samples were enriched with exosomes-like MVs. In addition, using flow cytometry we

detected the expression of CD9 and CD63, another exosome marker, in the surface of these

MVs. Interestingly, the expression of these markers was enriched in GBM patients’ MVs

(CD9=73.18%±10.02 and CD63=28.25±11.3) as compared with healthy control ones (CD9=

52.27±13.34; P<0.002 and CD63 10.16±2.7; P<0.001) (Fig 1D and E).

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Altogether our data showed that we were able to isolate exosome-like MVs from the serum of

GBM patients in a fast and reliable manner by using an adsorption method such as Exoquick.

Detection of Serum MVs miRNAs and their Association with GBM Diagnosis

In order to identify a tumor-derived miRNA signature in MVs isolated from the serum of GBM

patients we profiled the expression of 381 known miRNAs by using a microfluidic TaqMan

low density miRNA qRT-PCR array (TLDA) in 25 GBM patients matched by age and sex with

healthy donors. We generated a list of likely MVs-based miRNAs biomarker candidates for

GBM patients which satisfied the following criteria for additional individual qRT-PCR

validation: i) to be differentially expressed at least 2 Cts, and ii) to have a significant False

Discovery Rate (FDR; P<0.05). Overall, we found 7 miRNAs (miR483-5p, miR-574-3p, miR-

320, miR-197, miR-484, miR-146a and miR-223) and one small nuclear RNA (RNU6-1) that

met both criteria (Appendix Table A5, online only). These 8 sncRNAs were further subjected

to single qRT-PCR analyses to validate their expression in the same samples. This new

analysis confirmed the overexpression of RNU6-1, miR-320 and miR-574-3p in GBM patients

when subjected to single qRT-PCR. As shown in Table 1 and Fig 2A the expression levels of

the three sncRNAs were significantly different between the GBM patient group and the healthy

controls (P<0.0001 for RNU6-1; P =0.007 for miR-320; P <0.003 for miR-574-3p).

Intriguingly, the small nuclear RNA RNU6-1 was the one that resulted to be the most

upregulated in GBM patients (fold change, 2-ΔCT, = 387±1370) compared with healthy controls

(fold change=11.73±23.30; p<0.001). Next, we investigated whether the expression of each

single sncRNA or a combination of the three of them could have enough power to discriminate

between GBM patients and healthy controls. A machine learning algorithm based on logistic

regression was applied to the expression of each single sncRNA or the expression of the three

sncRNAs. The obtained classifier had a good diagnostic performance as shown by ROC

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analyses with an AUC of 0.926 (95% confidence interval (CI), 0.84 to 1; P <0.0001) for the

three sncRNA together, 0.852 (95% CI, 0.74 to 0.96; P <0.0001) for RNU6-1, 0.720 (95% CI,

0.56 to 0.87; P =0.0067) for miR-320, and 0.738 (95% CI, 0.58 to 0.89; P =0.0055) for miR-

574-3p (Fig 2B). At a cut-off value of 0.349 for the three sncRNAs, the sensitivity was 87%

and the specificity was 86%. At a cut-off value of 0.454 for RNU6-1, the sensitivity was 73%

and the specificity was 70%. At a cut-off values of 0.477 for miR-320, the sensitivity was 65%

and the specificity was 65%. At a cut-off value of 0.454 for miR-574-3p, the sensitivity was

59% and the specificity was 59%. These results suggested that either RNU6-1 alone or the

miR-320/miR-574-3p/RNU6-1 combined signature could have diagnostic value in GBM

patients.

Independent validation of the diagnostic value of the sncRNA signature

To further verify the diagnostic value of the sncRNAs signature identified in the previous

cohort, the expression of the three sncRNA was assessed on an independent group of 80 serum

MVs samples which included 50 GBM patients and 30 healthy controls (App Table A2, online

only). In this new cohort only the expression of RNU6-1 was significantly elevated in GBM

patients (fold change: 1732±11590) compared with healthy controls (fold change: 63.79±210;

P<0.001) (Fig 3A). We did not find significant differences between GBM patients and healthy

controls in the expression levels of miR-320 and miR-574-3p (P=0.592 and P=0.173,

respectively). We then proceed to evaluate the diagnostic potential of these 3 sncRNAs in this

validation cohort. ROC curve analyses revealed that the expression levels of either RNU6-1

alone or the combination of the three sncRNAs found in the MVs were useful and robust

biomarkers for differentiating GBM patients from healthy controls, with an AUC of 0.722

(95% CI, 0.60 to 0.84; P =0.0007) for RNU6-1 and 0.775 (95% CI, 0.65 to 0.90; P <0.0001)

for the three markers together (Fig3). Importantly, at a cut-off value 0.372 for RNU6-1, the

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sensitivity was 66% and the specificity was 68%. Meanwhile, at a cut-off value of 0.374 for

the three sncRNA signature, the sensitivity was 70% and the specificity was 71%. These data

indicate that the signature of the 3 sncRNA has slightly higher power to discriminate between

GBM patients and healthy controls (Fig 3B).

Altogether our results support the notion that the expression levels of RNU6-1, miR-320 and

miR-574-3p found in MVs isolated from the serum has the power to discriminate healthy

individuals from GBM patients therefore could serve as diagnostic biomarker.

Analysis of the Diagnostic Value of the sncRNAs Signature in Other Malignancies

In order to evaluate the specificity of our sncRNA signature for the diagnosis of GBM we

analyzed the expression of RNU6-1, miR-320 and miR-574-3p in a set of sera from patients

with brain metastases (N= 9; See App Table3) and a variety of gastrointestinal tumors (N=15;

see App Table4). We compared the expression levels of these three sncRNA in the above

samples with the previous cohorts of healthy controls and GBM patients. Of importance, our

data showed that the expression levels of these three sncRNAs were not de-regulated neither in

patients with of brain metastases nor in those with gastrointestinal tumors (RNU6-1, P=0.1604;

miR-320, P=0.2709; miR574-3p, P=0.6337; Kruskal-Wallis test; Fig 4). In spite of these small

sample sizes, our results suggested that differential expression of miR-320/miR574-3p/RNU6-

1 levels might be specific of MVs isolated from the serum of patients with GBM, thus

underscoring the diagnostic value of this signature.

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DISCUSSION

In this study, we demonstrate for the first time that a sncRNA signature of 2 miRNAs (miR-

320 and miR-574-3p) and one small nuclear RNA (RNU6-1) found in MVs isolated from the

serum of GBM patients could serve as a potential diagnostic biomarker. Although previous

studies suggested the utility of plasma or serum miRNAs in GBM 26-28, to our knowledge this

is the first report on the quantitative assessment of miRNAs in MVs isolated from GBM

patients’ sera.

There is a paucity of robust biomarkers in GBM that allow early detection and monitoring

response to treatment. In the last years circulating miRNAs have emerged as ideal candidate

biomarkers since they are stable in plasma and serum 3, their expression is deregulated in

cancer 29 and appear to be tissue specific 30. Moreover, the notion that solid tumors including

GBM 12,13 shed large quantities of small, membranous MVs into the circulation has opened the

possibility to use theses MVs as potential sources of biomarkers to predict both diagnosis and

response to therapy. Several studies have demonstrated that these MVs serve as biocargos for

proteins, including epidermal growth factor receptor (EGFR) and its variant EGFRviii 12,31, as

well as RNA 13 and DNA 32. Therefore, in this study we explore the possibility of finding a

specific miRNA signature in serum MVs that would allow discerning GBM patients from

healthy controls.

Previous studies have addressed the diagnostic potential of circulating miRNAs from different

sources in GBM 26-28, but neither of them used MVs as sample candidates. One of these studies

used the cellular fraction of the blood and identified several miRNAs upregulated in their

samples 26. This study differs with ours not only in the source of miRNAs but also in the fact

that the patients were already subjected to different therapies. A second study used the

cerebrospinal fluid (CSF) to identify GBM and metastatic brain cancer miRNAs that could

serve as biomarkers 27. In this case they used a candidate approach based on previous miRNA

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profiling data 13,33,34. They generated a classification tree of brain cancer patients that allowed

distinguishing GBM patients from non-neoplastic lesions, low-grade gliomas and brain

metastases. This work provides useful information regarding the type of tumor, however

obtaining CSF samples in patients with brain masses seems more much more inconvenient than

drawing a single blood sample. A third study identified 7 miRNAs in the serum of GBM

patients using sequencing and in silico analysis 28. Their signature differs widely with our

signature due probably not only to the source of miRNAs but also to the fact that they used a

sequencing approach.

In our study it was striking to see that RNU6-1 was the main biomarker candidate to

distinguish between GBM patients and healthy controls since this RNA is part of the splicing

machinery and resides in the nucleus 35. Interestingly, it has been shown that RNU6-1 is

synthetized by RNA polymerase III which is negatively regulated by tumor suppressors such

as Rb, p53 or PTEN 37-39 and its enhanced activity has been shown essential for tumorogenesis

40. Therefore it could be possible that in the context of RB, p53 or PTEN pathway dysfunction,

which are prevalent in GBM, 41 RNU6-1 is overexpressed. Supporting this notion,

U6:SNORD44 ratio was found to be consistently higher in the sera of breast cancer patient,

regardless of ER status 42. Furthermore, RNU2, a counterpart component of RNU6-1 in the

spliceosome, has recently being detected circulating in serum and proposed as novel diagnostic

biomarker for pancreatic ductal adenocarcinoma and colorectal carcinoma 43. Nevertheless,

further studies would be needed to elucidate the molecular mechanism of RNU6-1

upregulation in GBM serum MVs.

In the fullness of time, our study provides a sncRNA signature found in serum MVs that

discriminates GBM patients from healthy controls and that could hence serve as a diagnostic

tool.

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Acknowledgements

We thank Dr. Charles Lawrie (Instituto Biodonostia, SanSebastian, Spain) and Dr. Rubén Pío

(Center for the Applied Medical Research, Pamplona, Spain) for critical reading of the

manuscript

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FIGURE LEGENDS

Fig. 1 Characterization of microvesicles (MVs) isolated from GBM patients serum. (A)

Transmission electron microscopy analysis. Representative micrograph depicting MVs found

in the serum of GBM patients. Arrows point to membrane-like structures with different sizes;

(B) Determination of MVs size distribution by dynamic light scattering; (C) Evaluation by

western blot of the expression levels of several MVs/exosomes markers in total serum or in

MVs isolated from serum of either healthy controls (C) or GBM patient (P); (D) Determination

by flow cytometry of CD9 or CD63 expression on the surface of MVs isolated from healthy

controls or GBM patients serum.

Fig. 2 Detection of MVs small-non-coding RNAs (sncRNAs) and their association with GBM

diagnosis. (A) sncRNAs validation by individual quantitative reverse transcription polymerase

chain reaction in the training cohort. Expression levels of the individual sncRNAs are

normalized to RNU48. Fold change was calculated as the difference of the mean normalized

expression values between patients and controls MVs (−ΔCt = mean (ΔCt GBM patients) – –

mean (ΔCt controls)). A t-test analysis was performed with R/Bioconductor; (B) Receiver

operator characteristic curve (ROC) showing the true positive and false positive rate for the

training cohort for the three sncRNA signature or for each individual one.

Fig. 3 Validation of the diagnostic value of the sncRNA signature in an independent cohort.

(A) sncRNAs expression by individual quantitative reverse transcription polymerase chain

reaction in an independent cohort. Expression levels of the individual sncRNAs are normalized

to RNU48. Fold change was calculated as the difference of the mean normalized expression

values between patients and controls MVs (ΔCt = mean (ΔCt GBM patients) – –mean (ΔCt

controls)). A t-test analysis was performed with R/Bioconductor; (B) Receiver operator

characteristic curve (ROC) showing the true positive and false positive rate for the independent

cohort for the the three sncRNA signature or for RNU6-1 alone.

Manterola et al

20

Fig. 4 Analysis of RNU6-1/miR-320/miR-574-3p expression levels in other tumors. (A)

sncRNAs expression by individual quantitative reverse transcription polymerase chain reaction

in a cohort of metastasis to the brain (metastasis) and gastrointestinal tumors (GI) .Samples

were compared with the previous cohort of healthy controls and GBM patients. Expression

levels of the individual sncRNAs were calculated relative to corresponding RNU48. Fold

change was calculated as 2−ΔCt, were ΔCt= CTRNU48 – CTRNU6-1/miR-320/miR-574-3p. Statistically

significant differences were determined using a Kruskall-Wallis test.

<200 nm<50 nm

200 nm

A

TSG 101

LAMP1

CD9

C: HEALTHY CONTROL/ P: GBM PATIENT

ALIX

C

D

Figure 1

CD9

ControlN=10

GBMN=10

0

20

40

60

80

100

CD

9 ex

pres

sion

(%)

CD63

ControlN=10

GBMN=10

0

20

40

60

CD

63 e

xpre

ssio

n(%

)

E

Calnexin

P=0.002 P=0.001

157,9 – 210.8 nm

12,86-23.81 nmInte

nsity

(%)

Size (nm)

B

A

Figure 2

BRNU6 / miR−320 / miR−574−3p

False positive rate

True

pos

itive

rate

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

AUC = 0.926

p < 0.0001

RNU6

False positive rate

True

pos

itive

rate

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

AUC = 0.852

p < 0.0001

miR−320

False positive rate

True

pos

itive

rate

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

AUC = 0.719

p = 0.0067

miR−574-3p

False positive rate

True

pos

itive

rate

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

AUC = 0.738

p = 0.0055

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GBM Controls

−5

0

5

10

n=23 n=20

miR

−320

(−∆C

t)

p = 0.0076

•

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GBM Controls

−10

−5

0

5

n=22 n=17

miR

−574

−3p

(−∆C

t)

p = 0.0031

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GBM Controls

−5

0

5

10

n=24 n=22

RN

U6

(−∆C

t)

p < 0.001

A

Figure 3

BRNU6

False positive rate

True

pos

itive

rate

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

AUC = 0.722

p = 0.0007

RNU6 / miR−320 / miR−574−3p

False positive rate

True

pos

itive

rate

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

AUC = 0.775

p < 0.0001

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GBM Controls

−5

0

5

10

15

n=50 n=27

RN

U6

(−∆C

t)p = 0.0013

•

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GBM Controls

0

5

10

15

n=50 n=27

miR

−320

(−∆C

t)

p = 0.5927

•

•

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GBM Controls−15

−10

−5

0

5

10

15

n=47 n=27

miR

−574

−3p

(−∆C

t)

p = 0.173

Figure 4

ControlsN=28

GBMN=49

MetastasisN=9

GIN=15

10-2

10-1

100

101

102

103

104

105

106

107

RN

U6

(2-Δ

CT )

B

10-1

100

101

102

103

104

105

miR

-320

(2-Δ

CT )

ControlsN=27

GBMN=49

MetastasisN=9

GIN=15

miR

-574

-3p

(2-Δ

CT )

10-5

10-3

10-1

101

103

105

ControlsN=28

GBMN=46

MetastasisN=9

GIN=15

C

A

P=0.001

P=0.1604

P=0.592P=0.2709

P=0.173P=0.6337

Table 1. Differentially expressed small non-coding RNAs in serum MVs of 25 GBM samples compared to 25 healthy controls paired by age and sex, after validation using single qRT-PCR analyses (change > 2 Ct and t-test p-value < 0.01 as a cut-off level)

microRNA −ΔCt p-value RNU6 4.5 <0.0001 miR-320 2.5 0.0076 miR-574-3p 3.1 0.0031