Serum N-Glycomics
with Nano-LC-QToF LC-MS/MS
Reveals N-Glycan Biomarkers for Glioblastoma, Meningioma, and
High-Grade Meningioma
Introduction Brain tumor incidence
and mortality are growing rapidly worldwide,
driven by factors such as aging, population growth, risk factors,
and socioeconomic development. According to GLOBOCAN 2022, the incidence
rate for brain tumors is 3.9% (173,591 cases) for males and 3.1% (147,885)
for females, while the mortality rate is 3.0% (139,737 cases) for
males and 2.2% (108,568 cases) for females. Brain cancers include several types, notably glioblastomas and meningiomas. Glioblastomas are the most aggressive malignant
brain tumors, arising from glial cells. On the other hand, meningiomas,
which form from the arachnoid cap cells of the brain and spinal cord,
are usually benign at first, but often become more aggressive over
time. Based on the incidence of brain
tumors in the US, glioblastoma is the most common malignant brain
tumor, accounting for 48.6%. Among benign brain tumors, meningioma
is the most common and pituitary tumor is the second most common,
accounting for 53.9 and 24.0%, respectively. Typical diagnostic methods for brain tumors involve neuroimaging
and pathological analysis, but definitive diagnoses are made through
histological examination. Tumor
cells display a wide range of glycosylation alterations than
normal cells. Glycosylation, the addition
of glycans to proteins and lipids, occurs in the endoplasmic reticulum
(ER) or Golgi apparatus. This process is mediated by enzymes, i.e.,
glycosyltransferases and glycosidases, which depend on factors including
protein substrate bioavailability, enzyme activity, altered enzyme
location within subcellular components, and levels of gene transcription. Differential expression of glycosylation enzymes
between healthy cells and cancer cells highlights targeting of enzymes
involved in the biosynthesis of aberrant glycans in discovering anticancer
drugs. Two principal mechanisms contribute to tumor-associated glycans:
incomplete synthesis and neo-synthesis. During the early stages of cancer, incomplete synthesis disrupts
normal synthesis of complex glycans expressed in epithelial cells,
leading to the biosynthesis of truncated structures such as sialyl
Tn (STn). In advanced stages, neo-synthesis
involves cancer-associated genes that drive the expression of glycan
cancer biomarkers like sialyl Lewis a (SLe a )
and sialyl Lewis x (SLe x ). , Several cancer-associated glycosylation changes such as β1,6
branching, sialyl Lewis antigens, α2,6-sialylated lactosamine,
T, Tn, and STn antigens, and gangliosides/glycosphingolipids have
also been documented. Liquid chromatography
with tandem mass spectrometry (LC-MS/MS)-based
methods for the identification of serum glycan biomarkers for cancer
have been demonstrated previously, with methods focusing N-glycan
using nano-LC-QToF − and glycopeptides using UPLC-QqQ , − mass spectrometry. For example, the N-glycans, Hex 4 HexNac 5 Fuc 1 , and Hex 5 HexNAc 6 Fuc 1 , as well as the sugar composition signatures,
NeuAc2 and Gal4 were found to be biomarkers for lung cancer (AUC =
0.74, 95% CI: 0.68–0.80). In a
separate study, a panel of N-glycans—Hex 6 HexNAc 5 NeuAc 1 , Hex 6 HexNAc 5 NeuAc 3 , Hex 6 HexNAc 5 Fuc 2 NeuAc 1 , Hex 7 HexNAc 6 Fuc 1 NeuAc 3 , Hex 4 HexNAc 3 NeuAc 1 , and
Hex 5 HexNAc 4 Fuc 1 NeuAc—were
found to be biomarkers for nonsmall cell lung carcinoma (AUC = 0.959,
95% CI: 0.85–1.0). In this
study, we employed nano-LC-QToF N-glycomics to identify
N-glycan biomarkers in patients with GBM and MG. In the training data
sets, we identified N-glycan structures that were significantly different
between brain tumor types. We then constructed biomarker models from
this panel of N-glycan structures using logistic regression modeling.
These biomarker models were validated for their predictive accuracy
using a test data set and were found to perform better as an effective
diagnostic tool for brain tumors. 

Materials and Methods 2.1 Ethical Statement and Clinical Sample Collection The research study was approved by the Human Ethics Committee of
Khon Kaen University, Thailand, in accordance with the Declaration
of Helsinki and the ICH Good Clinical Practice Guidelines (HE651186).
Prior to recruitment, informed consent was obtained from the research
participants or their guardians. Brain tumor patients were diagnosed
via histopathological analysis at the Department of Pathology, Srinagarind
Hospital, Khon Kaen University, Thailand. Serum samples were obtained
from the patients with glioblastoma (GBM, n = 19),
meningioma (MG, n = 34), and pituitary tumor (PT, n = 3). Control serum samples were obtained from healthy
individuals ( n = 20) who presented with a normal
range of fasting blood sugar, liver function test (aspartate aminotransferase,
alanine transaminase, and alkaline phosphatase), and complete blood
counts. Detailed clinical and demographic information for the cohort
is provided in . 2.2 Serum N-Glycan Sample Preparation for N-Glycomic
Analysis Serum N-glycans for mass spectrometry analysis were
prepared following a previously established protocol. , Briefly, 25 μL (μL) of serum were reconstituted with
100 μL of PNGase F release buffer (100 mM NH 4 CO 3 , 5 mM dithiothreitol) and then heated in a 100 °C water
bath for 2 min (10 s on, 10 s off). After cooling down to room temperature,
2 μL of PNGase F solution (New England Biolabs) were added to
each serum sample and followed by incubation at 37 °C for 18
h. After overnight incubation, the samples were centrifuged at 42,000
rpm (4 °C) for 45 min. The supernatant, containing the released
N-glycan and salts, was cleaned-up using solid-phase extraction with
porous graphitized carbon (PGC) plates. Purified N-glycans were eluted
from the PGC plates using 40% acetonitrile (ACN) containing 0.05%
trifluoroacetic acid (TFA) and the solvents were removed in
vacuo prior to nano-LC-QToF analysis. 2.3 N-Glycomic Nano-LC-QToF Data Acquisition and
Analysis Purified serum N-glycans and quality control samples
(prepared from released N-glycans from 6 μg RNase B and 6 μg
standard serum solution) were reconstituted in 60 μL ultrapure
water and 5 μL were injected into an Agilent 6520 nano-LC-QToF
mass spectrometer (Agilent). Separation was performed using an Agilent
PGC-Chip II equipped with a 40 nL enrichment column and a 43 mm ×
75 μm analytical column (particle size 5 μm). The binary
solvent system consisted of mobile phase A (3% v/v ACN and 0.1% v/v
formic acid in water) and mobile phase B (90% v/v ACN and 1% v/v formic
acid in water). Chromatography was carried out using the gradient
as follows: 0–2.5 min, 1% B; 2.5–20 min, 16% B; 20–35
min, 58%; 35–40 min, 100% B; 40–50 min, 100% B; and
50.01–65 min, 0% B, with a flow rate of 0.3 μL/min. Tandem
mass spectra were acquired following collision-induced dissociation,
with spectra acquired at 0.8 s/spectrum in positive ion mode. Acquired LC-MS/MS data were processed using MassHunter Qualitative
Analysis software B.07 (Agilent) using the “Find by Molecular
Feature” algorithm to identify N-glycan compositions from an
in-house library of human N-glycans using a quality score of at least
30 and mass accuracy within 10 ppm. To further validate the identified
structures, we manually annotated the corresponding MS/MS spectra
were manually annotated. N-glycan quantification was carried out by
measuring the chromatographic peak area of validated N-glycans from
the extracted ion chromatograms in MassHunter, normalized to the total
ion chromatogram. N-glycan compounds were annotated as based on their
composition: Hex a HexNAc b Fuc c NeuAc d , where Hex a refers to the number of hexose (i.e.,
mannose, galactose), HexNAc b refers to the number of N -acetylhexosamine (i.e., N -acetylglucosamine),
Fuc c refers to the number of fucose, and NeuAc d refers to the number of neuraminic acid (i.e., sialic acid) residues.
N-glycans were subsequently classified using the following classification
system: high-mannose (Hex a HexNAc b , where b = 2 denotes the chitobiose core and “ a ” refers solely to mannose residues), undecorated (Hex a HexNAc b , where “ a ”
refers to both mannose and galactose residues), fucosylated (Hex a HexNAc b Fuc c , where c ≥ 1), sialylated (Hex a HexNAc b NeuAc d , where d ≥ 1), or sialofucosylated
(Hex a HexNAc b Fuc c NeuAc d , where both c and d ≥ 1). 2.4 Statistical Analysis Two-way ANOVA
(α = 0.05) was performed to compare the abundance of N-glycan
compositions and types (high-mannose, undecorated, fucosylated, sialylated,
sialofucosylated) followed by Tukey’s posthoc test (GraphPad
Prism, v. 10.2.3). Biomarker analysis was performed using MetaboAnalyst
6.0. Processed N-glycan abundance values
per category, healthy (HE), meningioma (MG), pituitary tumor (PT),
and glioblastoma (GBM), were uploaded to MetaboAnalyst 6.0 and then
analyzed using univariate and multivariate ROC analyses. Univariate
ROC analysis was obtained using the “Classical univariate ROC
curve analyses” module to calculate the ROC curves for individual
N-glycan compounds. Multivariate ROC analysis was conducted using
the “ROC curve-based model evaluation (Tester)” module,
to identify the top ten most important glycan features according to
VIP scoring method. To further optimize the model in achieving the
best AUC, the most important glycan features with high univariate
ROC analysis curves were used in developing the model, as determined
by the logistic regression (LR) algorithm. The LR algorithm was used
to construct the LR biomarker model from selected N-glycan features. 

Ethical Statement and Clinical Sample Collection The research study was approved by the Human Ethics Committee of
Khon Kaen University, Thailand, in accordance with the Declaration
of Helsinki and the ICH Good Clinical Practice Guidelines (HE651186).
Prior to recruitment, informed consent was obtained from the research
participants or their guardians. Brain tumor patients were diagnosed
via histopathological analysis at the Department of Pathology, Srinagarind
Hospital, Khon Kaen University, Thailand. Serum samples were obtained
from the patients with glioblastoma (GBM, n = 19),
meningioma (MG, n = 34), and pituitary tumor (PT, n = 3). Control serum samples were obtained from healthy
individuals ( n = 20) who presented with a normal
range of fasting blood sugar, liver function test (aspartate aminotransferase,
alanine transaminase, and alkaline phosphatase), and complete blood
counts. Detailed clinical and demographic information for the cohort
is provided in . 

Serum N-Glycan Sample Preparation for N-Glycomic
Analysis Serum N-glycans for mass spectrometry analysis were
prepared following a previously established protocol. , Briefly, 25 μL (μL) of serum were reconstituted with
100 μL of PNGase F release buffer (100 mM NH 4 CO 3 , 5 mM dithiothreitol) and then heated in a 100 °C water
bath for 2 min (10 s on, 10 s off). After cooling down to room temperature,
2 μL of PNGase F solution (New England Biolabs) were added to
each serum sample and followed by incubation at 37 °C for 18
h. After overnight incubation, the samples were centrifuged at 42,000
rpm (4 °C) for 45 min. The supernatant, containing the released
N-glycan and salts, was cleaned-up using solid-phase extraction with
porous graphitized carbon (PGC) plates. Purified N-glycans were eluted
from the PGC plates using 40% acetonitrile (ACN) containing 0.05%
trifluoroacetic acid (TFA) and the solvents were removed in
vacuo prior to nano-LC-QToF analysis. 

N-Glycomic Nano-LC-QToF Data Acquisition and
Analysis Purified serum N-glycans and quality control samples
(prepared from released N-glycans from 6 μg RNase B and 6 μg
standard serum solution) were reconstituted in 60 μL ultrapure
water and 5 μL were injected into an Agilent 6520 nano-LC-QToF
mass spectrometer (Agilent). Separation was performed using an Agilent
PGC-Chip II equipped with a 40 nL enrichment column and a 43 mm ×
75 μm analytical column (particle size 5 μm). The binary
solvent system consisted of mobile phase A (3% v/v ACN and 0.1% v/v
formic acid in water) and mobile phase B (90% v/v ACN and 1% v/v formic
acid in water). Chromatography was carried out using the gradient
as follows: 0–2.5 min, 1% B; 2.5–20 min, 16% B; 20–35
min, 58%; 35–40 min, 100% B; 40–50 min, 100% B; and
50.01–65 min, 0% B, with a flow rate of 0.3 μL/min. Tandem
mass spectra were acquired following collision-induced dissociation,
with spectra acquired at 0.8 s/spectrum in positive ion mode. Acquired LC-MS/MS data were processed using MassHunter Qualitative
Analysis software B.07 (Agilent) using the “Find by Molecular
Feature” algorithm to identify N-glycan compositions from an
in-house library of human N-glycans using a quality score of at least
30 and mass accuracy within 10 ppm. To further validate the identified
structures, we manually annotated the corresponding MS/MS spectra
were manually annotated. N-glycan quantification was carried out by
measuring the chromatographic peak area of validated N-glycans from
the extracted ion chromatograms in MassHunter, normalized to the total
ion chromatogram. N-glycan compounds were annotated as based on their
composition: Hex a HexNAc b Fuc c NeuAc d , where Hex a refers to the number of hexose (i.e.,
mannose, galactose), HexNAc b refers to the number of N -acetylhexosamine (i.e., N -acetylglucosamine),
Fuc c refers to the number of fucose, and NeuAc d refers to the number of neuraminic acid (i.e., sialic acid) residues.
N-glycans were subsequently classified using the following classification
system: high-mannose (Hex a HexNAc b , where b = 2 denotes the chitobiose core and “ a ” refers solely to mannose residues), undecorated (Hex a HexNAc b , where “ a ”
refers to both mannose and galactose residues), fucosylated (Hex a HexNAc b Fuc c , where c ≥ 1), sialylated (Hex a HexNAc b NeuAc d , where d ≥ 1), or sialofucosylated
(Hex a HexNAc b Fuc c NeuAc d , where both c and d ≥ 1). 

Statistical Analysis Two-way ANOVA
(α = 0.05) was performed to compare the abundance of N-glycan
compositions and types (high-mannose, undecorated, fucosylated, sialylated,
sialofucosylated) followed by Tukey’s posthoc test (GraphPad
Prism, v. 10.2.3). Biomarker analysis was performed using MetaboAnalyst
6.0. Processed N-glycan abundance values
per category, healthy (HE), meningioma (MG), pituitary tumor (PT),
and glioblastoma (GBM), were uploaded to MetaboAnalyst 6.0 and then
analyzed using univariate and multivariate ROC analyses. Univariate
ROC analysis was obtained using the “Classical univariate ROC
curve analyses” module to calculate the ROC curves for individual
N-glycan compounds. Multivariate ROC analysis was conducted using
the “ROC curve-based model evaluation (Tester)” module,
to identify the top ten most important glycan features according to
VIP scoring method. To further optimize the model in achieving the
best AUC, the most important glycan features with high univariate
ROC analysis curves were used in developing the model, as determined
by the logistic regression (LR) algorithm. The LR algorithm was used
to construct the LR biomarker model from selected N-glycan features. 

Results 3.1 Serum N-Glycans are Different among Patients
with Brain Tumor and Healthy Controls N-Glycans obtained
from the sera of patients with brain tumors (34 meningioma, 19 glioblastoma,
and 3 pituitary tumor) and healthy individuals ( n = 20) were subjected to the LC-MS/MS N-glycomics method, quantifying
more than 400 N-glycan compounds ( A,B). Analysis of N-glycan abundances revealed
that the sera were primarily composed of sialylated N-glycans (>40%)
( C). Statistical
comparison of N-glycan abundances classified by type (high-mannose,
undecorated, fucosylated, sialylated, and sialofucosylated) revealed
significant differences in the abundance of fucosylated and sialylated
compounds. Specifically, the total abundance of fucosylated compounds
was significantly lower in glioblastoma patients compared with healthy
controls ( p = 0.0036). On the other hand, the total
abundance of sialylated compounds was significantly higher in glioblastoma
( p = 0.0033) and meningioma ( p =
0.0130) patients compared to healthy individuals. Based on these results,
further statistical tests were performed to construct biomarker models
for glioblastoma and meningioma by dividing the data sets into training
and testing sets in MetaboAnalyst ( D). 3.2 Sialylated and Fucosylated Serum N-Glycan
Biomarkers for the Detection of Glioblastoma We first constructed
biomarker models for glioblastoma (GBM) patients. From the training
set ( n = 57), non-GBM samples (composed of HE, MG,
and PT; n = 42) were separated from GBM samples ( n = 15), and N-glycan biomarkers were identified by using
ROC curve analyses ( A). Classical univariate ROC analyses using MetaboAnalyst
revealed 2 significantly overexpressed N-glycans in GBM: 6_4_0_2 ( p = 0.0007) and 10_2_0_0 ( p = 0.0003) ( B and ). Conversely, the following
compounds were underexpressed in GBM: 4_4_1_0 ( p =
0.0003), 6_6_1_2 ( p = 0.0116), 3_3_0_0 ( p = 0.0123), 4_4_1_1 ( p = 0.0044), 3_3_1_0 ( p = 0.0088), 5_4_1_0 ( p = 0.0087), 4_3_1_0
( p = 0.0052), and 4_4_0_0 ( p = 0.0284)
( B and ). From these significantly expressed N-glycans, 6_4_0_2
(overexpressed)
and 4_4_1_0 (underexpressed) provided the best diagnostic performance
for GBM versus non-GBM samples, with AUC values of 0.8571 (78.95%
sensitivity, 77.19 specificity) and 0.800 (68.42% sensitivity, 82.46%
specificity), respectively ( C,D and ). These two compounds were used to construct a logistic regression
(LR) model to determine the combined glycan index (CGI) for GBM Calculating the CGI GBM for healthy
(HE), glioblastoma (GBM), and non-GBM samples showed significantly
higher CGI GBM value for GBM compared to HE ( p = 0.0027) and non-GBM ( p < 0.0001) ( E). Furthermore,
using the CGI GBM with both 6_4_0_2 and 4_4_1_0 improved
the diagnostic capability, with an AUC CGI,GBM = 0.866 (78.95%
sensitivity, 84.21% specificity) ( F and ). Thus, a serum N-glycan panel of 6_4_0_2 and 4_4_1_0
can be used as biomarkers for the detection of glioblastoma. 3.3 Sialofucosylated and Fucosylated Serum N-Glycan
Biomarkers for the Detection of Meningioma Next, we constructed
biomarker models for meningioma (MG) patients. From the training set
( n = 57), non-MG samples (composed of HE, GBM, and
PT; n = 32) were separated from MG samples ( n = 25), and N-glycan biomarkers were identified using ROC
curve analyses ( A). In MetaboAnalyst, we conducted classical univariate ROC analyses
to determine the significantly different individual N-glycans and
found five compounds to be overexpressed in MG: 4_4_1_1 ( p = 0.0029), 6_3_1_1 ( p = 0.0068), 8_7_0_5 ( p = 0.0026), 4_3_1_1 ( p = 0.0035), and
6_6_1_2 ( p = 0.0050) ( B and ). Additionally, five compounds were also significantly
underexpressed in MG: 6_6_2_0 ( p = 0.0001), 5_5_2_0
( p < 0.0001), 5_6_0_0 ( p = 0.0040),
6_6_3_0 ( p = 0.0017), and 8_8_0_5 ( p = 0.0013) ( B and ). Among these, we found that the overexpressed 4_4_1_1
and underexpressed
6_6_2_0 and 5_5_2_0 compounds yielded the best AUC for discriminating
between MG and non-MG samples, with AUC values of 0.7263 (73.53% sensitivity,
61.90% specificity), 0.8356 (82.35% sensitivity, 76.19% specificity),
and 0.8200 (76.47% sensitivity, 66.67% specificity), respectively
( C,D and ). These three N-glycans
were incorporated to the logistic regression (LR) model for determining
the combined glycan index (CGI) for MG Calculating the CGI MG for healthy
(HE), meningioma (MG), and non-MG samples showed significantly higher
CGI MG value for MG compared to HE ( p <
0.0001) and non-MG ( p < 0.0001) ( E). Furthermore, using the
CGI MG with the N-glycan panel 6_6_2_0, 5_5_2_0, and 4_4_1_0
improved the diagnostic capability, with an AUC CGI,MG =
0.9260 (82.35% sensitivity, 78.57% specificity) ( F and ). Thus, a serum N-glycan panel of 6_6_2_0,
5_5_2_0, and 4_4_1_0 can be used as biomarkers for the detection of
meningioma. 3.4 Fucosylated, Sialofucosylated, and Undecorated
Serum N-Glycan Biomarkers for the Detection of High-Grade Meningiomas
(WHO Grades II and III) We also constructed biomarker models
for high-grade meningioma (MG) patients, specifically WHO grades II
and III; from the training set ( n = 57), we separated
out control samples composed of grade I MG (MG1, n = 10) and non-MG (HE, n = 15; GBM, n = 15; PT, n = 2), from grade II/III MG (MG2/3, n = 14) and then identified N-glycan biomarkers using ROC
curve analyses ( A). In MetaboAnalyst, we conducted classical univariate ROC analyses
to determine the significantly different individual N-glycans and
found six compounds to be overexpressed in MG2/3:5_5_1_0 ( p = 0.0064), 4_3_0_0 ( p = 0.0029), 6_5_1_0
( p = 0.0008), 6_4_0_0 ( p = 0.0045),
7_7_4_4 ( p = 0.0002), and 5_5_1_1 ( p = 0.0216) ( B and ). On
the other hand, four compounds were significantly underexpressed in
MG2/3:7_7_4_3 ( p = 0.0007), 5_4_0_2 ( p = 0.0203), 7_4_0_0 ( p = 0.0057), and 5_4_1_2 ( p = 0.0130) ( B and ). From these overexpressed and underexpressed compounds,
we found
that the overexpressed 5_5_1_0 and 4_3_0_0, and underexpressed 7_7_4_3
compounds yielded the best AUC for discriminating between MG2/3 and
control samples, with AUC 5_5_1_0 = 0.7778 (65.00% sensitivity,
71.43% specificity), AUC 4_3_0_0 = 0.7587 (80.00% sensitivity,
58.93% specificity), and AUC 7_7_4_3 = 0.7651 (95.00% sensitivity,
51.79% specificity) ( C,D and ). Thus, these three compounds were used as an N-glycan panel to
construct logistic regression (LR) models for determining the combined
glycan index (CGI) of MG2/3 Calculating the CGI MG2/3 for healthy
(HE), non-MG (PT/GBM), meningioma grade I (MG1) and meningioma grades
II/III (MG2/3) samples showed significantly higher CGI MG2/3 value for MG2/3 compared to HE ( p = 0.0009), non-MG
( p = 0.0002), and MG1 ( p < 0.0001)
( E). Furthermore,
using the CGI MG2/3 with the N-glycan panel 5_5_1_0, 4_3_0_0,
and 7_7_4_3 improved the diagnostic capability, with an AUC CGI,MG2/3 = 0.8680 (70.00% sensitivity, 82.14% specificity) ( F and ). Thus, a serum N-glycan panel of 5_5_1_0,
4_3_0_0, and 7_7_4_3 can be used as biomarkers for the detection of
high-grade meningioma (WHO grades II and III). 3.5 Combined Serum N-Glycans for the Improved
Detection of Brain Tumors–Glioblastoma, Meningioma, and High-Grade
Meningioma After the construction and validation of biomarkers
for glioblastoma (AUC CGI,GBM = 0.866), meningioma (AUC CGI,MG = 0.9260), and high-grade meningioma (AUC CGI,MG2/3 = 0.8680), we tested the models for disease prediction in a training
set . In the GBM testing set (non-GBM n = 14; GBM n = 4), we validated that indeed the
abundance of the compound
6_4_0_2 ( p = 0.0322) was significantly higher in
GBM while 4_4_1_0 ( p = 0.0031) was significantly
lower, while the CGI GBM of GBM samples were significantly
higher than non-GBM samples ( p < 0.0001) ( A). In the
MG testing set (non-MG n = 10; MG n = 9), we found no statistical significance between the
non-MG and MG samples for the abundances of 6_6_2_0 ( p = 0.2925) and 4_4_1_1 ( p = 0.4057). However, compound
5_5_2_0 was significantly lower in MG than in non-MG samples ( p = 0.0870). Similarly, the CGI MG of MG samples
were significantly higher than non-MG samples ( p =
0.0832) ( B). Finally, in the MG2/3 testing set (control n =
14; MG2/3 n = 5), compounds 5_5_1_0 ( p = 0.2071), 7_7_4_3 ( p = 0.3440), and 4_3_0_0 ( p = 0.2159) were not significantly different in abundance
between MG2/3 and control samples. Likewise, the CGI MG2/3 of the control and MG2/3 samples were not significantly different
( C). 

Serum N-Glycans are Different among Patients
with Brain Tumor and Healthy Controls N-Glycans obtained
from the sera of patients with brain tumors (34 meningioma, 19 glioblastoma,
and 3 pituitary tumor) and healthy individuals ( n = 20) were subjected to the LC-MS/MS N-glycomics method, quantifying
more than 400 N-glycan compounds ( A,B). Analysis of N-glycan abundances revealed
that the sera were primarily composed of sialylated N-glycans (>40%)
( C). Statistical
comparison of N-glycan abundances classified by type (high-mannose,
undecorated, fucosylated, sialylated, and sialofucosylated) revealed
significant differences in the abundance of fucosylated and sialylated
compounds. Specifically, the total abundance of fucosylated compounds
was significantly lower in glioblastoma patients compared with healthy
controls ( p = 0.0036). On the other hand, the total
abundance of sialylated compounds was significantly higher in glioblastoma
( p = 0.0033) and meningioma ( p =
0.0130) patients compared to healthy individuals. Based on these results,
further statistical tests were performed to construct biomarker models
for glioblastoma and meningioma by dividing the data sets into training
and testing sets in MetaboAnalyst ( D). 

Sialylated and Fucosylated Serum N-Glycan
Biomarkers for the Detection of Glioblastoma We first constructed
biomarker models for glioblastoma (GBM) patients. From the training
set ( n = 57), non-GBM samples (composed of HE, MG,
and PT; n = 42) were separated from GBM samples ( n = 15), and N-glycan biomarkers were identified by using
ROC curve analyses ( A). Classical univariate ROC analyses using MetaboAnalyst
revealed 2 significantly overexpressed N-glycans in GBM: 6_4_0_2 ( p = 0.0007) and 10_2_0_0 ( p = 0.0003) ( B and ). Conversely, the following
compounds were underexpressed in GBM: 4_4_1_0 ( p =
0.0003), 6_6_1_2 ( p = 0.0116), 3_3_0_0 ( p = 0.0123), 4_4_1_1 ( p = 0.0044), 3_3_1_0 ( p = 0.0088), 5_4_1_0 ( p = 0.0087), 4_3_1_0
( p = 0.0052), and 4_4_0_0 ( p = 0.0284)
( B and ). From these significantly expressed N-glycans, 6_4_0_2
(overexpressed)
and 4_4_1_0 (underexpressed) provided the best diagnostic performance
for GBM versus non-GBM samples, with AUC values of 0.8571 (78.95%
sensitivity, 77.19 specificity) and 0.800 (68.42% sensitivity, 82.46%
specificity), respectively ( C,D and ). These two compounds were used to construct a logistic regression
(LR) model to determine the combined glycan index (CGI) for GBM Calculating the CGI GBM for healthy
(HE), glioblastoma (GBM), and non-GBM samples showed significantly
higher CGI GBM value for GBM compared to HE ( p = 0.0027) and non-GBM ( p < 0.0001) ( E). Furthermore,
using the CGI GBM with both 6_4_0_2 and 4_4_1_0 improved
the diagnostic capability, with an AUC CGI,GBM = 0.866 (78.95%
sensitivity, 84.21% specificity) ( F and ). Thus, a serum N-glycan panel of 6_4_0_2 and 4_4_1_0
can be used as biomarkers for the detection of glioblastoma. 

Sialofucosylated and Fucosylated Serum N-Glycan
Biomarkers for the Detection of Meningioma Next, we constructed
biomarker models for meningioma (MG) patients. From the training set
( n = 57), non-MG samples (composed of HE, GBM, and
PT; n = 32) were separated from MG samples ( n = 25), and N-glycan biomarkers were identified using ROC
curve analyses ( A). In MetaboAnalyst, we conducted classical univariate ROC analyses
to determine the significantly different individual N-glycans and
found five compounds to be overexpressed in MG: 4_4_1_1 ( p = 0.0029), 6_3_1_1 ( p = 0.0068), 8_7_0_5 ( p = 0.0026), 4_3_1_1 ( p = 0.0035), and
6_6_1_2 ( p = 0.0050) ( B and ). Additionally, five compounds were also significantly
underexpressed in MG: 6_6_2_0 ( p = 0.0001), 5_5_2_0
( p < 0.0001), 5_6_0_0 ( p = 0.0040),
6_6_3_0 ( p = 0.0017), and 8_8_0_5 ( p = 0.0013) ( B and ). Among these, we found that the overexpressed 4_4_1_1
and underexpressed
6_6_2_0 and 5_5_2_0 compounds yielded the best AUC for discriminating
between MG and non-MG samples, with AUC values of 0.7263 (73.53% sensitivity,
61.90% specificity), 0.8356 (82.35% sensitivity, 76.19% specificity),
and 0.8200 (76.47% sensitivity, 66.67% specificity), respectively
( C,D and ). These three N-glycans
were incorporated to the logistic regression (LR) model for determining
the combined glycan index (CGI) for MG Calculating the CGI MG for healthy
(HE), meningioma (MG), and non-MG samples showed significantly higher
CGI MG value for MG compared to HE ( p <
0.0001) and non-MG ( p < 0.0001) ( E). Furthermore, using the
CGI MG with the N-glycan panel 6_6_2_0, 5_5_2_0, and 4_4_1_0
improved the diagnostic capability, with an AUC CGI,MG =
0.9260 (82.35% sensitivity, 78.57% specificity) ( F and ). Thus, a serum N-glycan panel of 6_6_2_0,
5_5_2_0, and 4_4_1_0 can be used as biomarkers for the detection of
meningioma. 

Fucosylated, Sialofucosylated, and Undecorated
Serum N-Glycan Biomarkers for the Detection of High-Grade Meningiomas
(WHO Grades II and III) We also constructed biomarker models
for high-grade meningioma (MG) patients, specifically WHO grades II
and III; from the training set ( n = 57), we separated
out control samples composed of grade I MG (MG1, n = 10) and non-MG (HE, n = 15; GBM, n = 15; PT, n = 2), from grade II/III MG (MG2/3, n = 14) and then identified N-glycan biomarkers using ROC
curve analyses ( A). In MetaboAnalyst, we conducted classical univariate ROC analyses
to determine the significantly different individual N-glycans and
found six compounds to be overexpressed in MG2/3:5_5_1_0 ( p = 0.0064), 4_3_0_0 ( p = 0.0029), 6_5_1_0
( p = 0.0008), 6_4_0_0 ( p = 0.0045),
7_7_4_4 ( p = 0.0002), and 5_5_1_1 ( p = 0.0216) ( B and ). On
the other hand, four compounds were significantly underexpressed in
MG2/3:7_7_4_3 ( p = 0.0007), 5_4_0_2 ( p = 0.0203), 7_4_0_0 ( p = 0.0057), and 5_4_1_2 ( p = 0.0130) ( B and ). From these overexpressed and underexpressed compounds,
we found
that the overexpressed 5_5_1_0 and 4_3_0_0, and underexpressed 7_7_4_3
compounds yielded the best AUC for discriminating between MG2/3 and
control samples, with AUC 5_5_1_0 = 0.7778 (65.00% sensitivity,
71.43% specificity), AUC 4_3_0_0 = 0.7587 (80.00% sensitivity,
58.93% specificity), and AUC 7_7_4_3 = 0.7651 (95.00% sensitivity,
51.79% specificity) ( C,D and ). Thus, these three compounds were used as an N-glycan panel to
construct logistic regression (LR) models for determining the combined
glycan index (CGI) of MG2/3 Calculating the CGI MG2/3 for healthy
(HE), non-MG (PT/GBM), meningioma grade I (MG1) and meningioma grades
II/III (MG2/3) samples showed significantly higher CGI MG2/3 value for MG2/3 compared to HE ( p = 0.0009), non-MG
( p = 0.0002), and MG1 ( p < 0.0001)
( E). Furthermore,
using the CGI MG2/3 with the N-glycan panel 5_5_1_0, 4_3_0_0,
and 7_7_4_3 improved the diagnostic capability, with an AUC CGI,MG2/3 = 0.8680 (70.00% sensitivity, 82.14% specificity) ( F and ). Thus, a serum N-glycan panel of 5_5_1_0,
4_3_0_0, and 7_7_4_3 can be used as biomarkers for the detection of
high-grade meningioma (WHO grades II and III). 

Combined Serum N-Glycans for the Improved
Detection of Brain Tumors–Glioblastoma, Meningioma, and High-Grade
Meningioma After the construction and validation of biomarkers
for glioblastoma (AUC CGI,GBM = 0.866), meningioma (AUC CGI,MG = 0.9260), and high-grade meningioma (AUC CGI,MG2/3 = 0.8680), we tested the models for disease prediction in a training
set . In the GBM testing set (non-GBM n = 14; GBM n = 4), we validated that indeed the
abundance of the compound
6_4_0_2 ( p = 0.0322) was significantly higher in
GBM while 4_4_1_0 ( p = 0.0031) was significantly
lower, while the CGI GBM of GBM samples were significantly
higher than non-GBM samples ( p < 0.0001) ( A). In the
MG testing set (non-MG n = 10; MG n = 9), we found no statistical significance between the
non-MG and MG samples for the abundances of 6_6_2_0 ( p = 0.2925) and 4_4_1_1 ( p = 0.4057). However, compound
5_5_2_0 was significantly lower in MG than in non-MG samples ( p = 0.0870). Similarly, the CGI MG of MG samples
were significantly higher than non-MG samples ( p =
0.0832) ( B). Finally, in the MG2/3 testing set (control n =
14; MG2/3 n = 5), compounds 5_5_1_0 ( p = 0.2071), 7_7_4_3 ( p = 0.3440), and 4_3_0_0 ( p = 0.2159) were not significantly different in abundance
between MG2/3 and control samples. Likewise, the CGI MG2/3 of the control and MG2/3 samples were not significantly different
( C). 

Discussion Aberrant glycosylation has
been previously linked with brain cancer
pathology. − Several glycan vaccines, targeting glycosphingolipids,
glycoproteins, and proteoglycans, have been developed for tumor immunotherapy.
For glycoproteins, the T n , ST n , and TF antigens have been reportedly overexpressed
in several cancer types including brain, bladder, colorectal, ovarian,
and breast. , Identification of circulating
markers that are applicable for diagnosis and prognosis is still a
challenging task for the management of brain tumors. Currently, no
clinically validated circulating biomarkers available for brain tumors,
this might be due to blood-brain barrier that restrict the transportation
between the brain and blood. Recently,
many research groups persevere to identify the circulating markers
in the management of brain tumors. − With LC-MS/MS
N-glycomics, we were able to quantify the differences
in serum N-glycan expression between cancer patients and healthy controls
at a molecular structural level. As such, we generated a panel of
N-glycan compounds for the detection of a specific cancer. We have
previously applied this method to ovarian cancer, lung cancer, , and Alzheimer’s
disease, and we have applied the method
herein for multiple brain cancer types–glioblastoma, meningioma,
and high-grade meningioma . Due to the pathological differences among these
brain tumor types, we observed distinct N-glycan structures in the
biomarker discovery panel for each. In glioblastoma, sialylated (6_4_0_2)
and fucosylated (4_4_1_0) structures were significantly altered, whereas
in meningioma, sialofucosylated (4_4_1_1), and fucosylated (6_6_2_0
and 5_5_2_0) structures were significantly different. Between low-grade
meningioma (grade I) and high-grade meningioma (grades II and III),
significant differences were observed in undecorated (4_3_0_0), fucosylated
(5_5_1_0), and sialofucosylated (7_7_4_3) structures. Notably, some
striking differences and similarities emerged in the structures and
compositions of these N-glycans. The fucosylated biantennary structure
4_4_1_0 is underexpressed in glioblastoma, but its sialofucosylated
counterpart 4_4_1_1 is significantly overexpressed in meningioma sera.
The bisecting fucosylated structure 5_5_1_0 is overexpressed in high-grade
meningioma, and its diffucosylated counterpart 5_5_2_0 is underexpressed
in meningioma. Since changes in N-glycan expression reflect alterations
in protein glycosylation machinery, it would be interesting to correlate
these findings with glycoproteomics data to determine the serum glycoproteins
bearing these distinct N-glycan structures in brain tumor patients. Similar to our study, the data previously presented
by Varadi et
al. showed that sialylated N-glycans were elevated in the serum of
glioblastoma and meningioma patients compared to healthy controls. Increased sialylation in cancer cells and serum
was associated with disease progression and poor clinical outcomes
in cancer patients. − Additionally, sialylation has been implicated in the survival of
glioblastoma stem cells. Identifying
and targeting the sialyltransferase enzymes associated with glioblastoma
and meningioma could provide an alternative treatment approach for
the diseases. Furthermore, a decrease in core-fucosylated N-glycan
in the serum of glioblastoma and meningioma patients has also been
demonstrated; this information suggests
an alteration in core-fucosylation in brain tumors, particularly glioblastoma
and meningioma. Further functional studies of core-fucosylation in
brain tumors are warranted. Moreover, to enhance the efficiency of
disease detection, the serum N-glycoproteins that carry these glycans
should be further characterized. This information may deepen our understanding
of the tumor biology of glioblastoma and meningioma. 

Conclusions In summary, we utilized
nano-LC-QToF N-glycomics methods to identify
significantly different N-glycan compounds in the sera of patients
that can be used as biomarkers for the detection of glioblastoma and
meningioma. 

