investigation of serum proteome alterations in human glioblastoma multiforme

2378 Proteomics 2012, 12, 2378–2390DOI 10.1002/pmic.201200002

RESEARCH ARTICLE

Investigation of serum proteome alterations in human

glioblastoma multiforme

Kishore Gollapalli1, Sandipan Ray1, Rajneesh Srivastava1, Durairaj Renu2, Prateek Singh2,Snigdha Dhali3, Jyoti Bajpai Dikshit2, Rapole Srikanth3, Aliasgar Moiyadi4 and SanjeevaSrivastava1

1 Department of Biosciences and Bioengineering, Wadhwani Research Center for Biosciences and Bioengineering,Indian Institute of Technology Bombay, Powai, Mumbai, India

2 Strand Life Sciences Pvt. Ltd., Kirloskar Business Park, Hebbal, Bangalore, India3 Proteomics Laboratory, National Centre for Cell Science, Ganeshkhind, Pune, India4 Department of Neurosurgery, Advanced Center for Treatment Research and Education in Cancer, Tata MemorialCenter, Kharghar, Navi Mumbai, India

Glioblastoma multiforme (GBM) or grade IV astrocytoma is the most common and lethal adultmalignant brain tumor. The present study was conducted to investigate the alterations in theserum proteome in GBM patients compared to healthy controls. Comparative proteomic anal-ysis was performed employing classical 2DE and 2D-DIGE combined with MALDI TOF/TOFMS and results were further validated through Western blotting and immunoturbidimetric as-say. Comparison of the serum proteome of GBM and healthy subjects revealed 55 differentiallyexpressed and statistically significant (p <0.05) protein spots. Among the identified proteins,haptoglobin, plasminogen precursor, apolipoprotein A-1 and M, and transthyretin are verysignificant due to their functional consequences in glioma tumor growth and migration, andcould further be studied as glioma biomarkers and grade-specific protein signatures. Anal-ysis of the lipoprotein pattern indicated elevated serum levels of cholesterol, triacylglycerol,and low-density lipoproteins in GBM patients. Functional pathway analysis was performedusing multiple software including ingenuity pathway analysis (IPA), protein analysis throughevolutionary relationships (PANTHER), database for annotation, visualization and integrateddiscovery (DAVID), and GeneSpring to investigate the biological context of the identified pro-teins, which revealed the association of candidate proteins in a few essential physiologicalpathways such as intrinsic prothrombin activation pathway, plasminogen activating cascade,coagulation system, glioma invasiveness signaling, and PI3K signaling in B lymphocytes. Asubset of the differentially expressed proteins was applied to build statistical sample class pre-diction models for discrimination of GBM patients and healthy controls employing partial leastsquares discriminant analysis (PLS-DA) and other machine learning methods such as supportvector machine (SVM), Decision Tree and Naıve Bayes, and excellent discrimination betweenGBM and control groups was accomplished.

Keywords:

Biomedicine / 2D-DIGE / Glioblastoma multiforme / Glioma / Serum biomarker

Received: January 3, 2012Revised: April 9, 2012

Accepted: April 23, 2012

Correspondence: Professor Sanjeeva Srivastava, Department ofBiosciences and Bioengineering, IIT Bombay, Mumbai 400 076,IndiaE-mail: [email protected]: +91-22-2572-3480

Abbreviations: DT, Decision Tree; GBM, glioblastoma multi-forme; HC, healthy control; IPA, ingenuity pathway analysis; NB,Naive Bayes; PLS-DA, partial least squares discriminant analysis;SVM, support vector machine

1 Introduction

Uncontrolled proliferation of the glial cells result in the for-mation of tumors in brain; known as gliomas, which is con-sidered as one of the prime causes of cancer-related fatality[1, 2]. According to the WHO classification, gliomas can be

Colour Online: See the article online to view Figs. 1–4 in colour.

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Proteomics 2012, 12, 2378–2390 2379

divided into four different grades on the basis of their malig-nancy and histological characteristics [3], among which thegrade IV gliomas, commonly known as glioblastoma multi-forme (GBM) are the most aggressive and lethal type with anaverage life expectancy of patients 1 year or less from the timeof disease diagnosis [4]. The reason behind such lethality isuncontrolled proliferation of these glial tumor cells withinthe close confines of the bony cranium resulting in deathof nearby neural cells [5]. The prominent infiltrative growthof the tumors also hinders their efficient eradication, whichmakes the disease almost incurable with the currently avail-able therapeutic armamentarium. Although the combinedapplication of surgical resection, radiation, and concomitantchemotherapy has resulted in improved survival rates, thisbenefit is modest at best [6]. The prognosis and treatmentof GBM still remains challenging and needs to be exploredfurther.

Early and accurate diagnosis is a prime requirement forthe effective treatment and management of GBM. Although,presently noninvasive imaging techniques such as MRI areroutinely applied for GBM diagnosis, evaluation of minordifference in tumor size and behavior remains difficult tomeasure with these imaging approaches. Advanced MR tech-niques (such as spectroscopy and perfusion), though useful,are still limited in their ability to differentiate true recurrencesfrom treatment-induced changes and in prediction of malig-nant transformation from a low-grade glioma [7]. Tissue diag-nosis (by invasive means) may not always be feasible. In orderto circumvent the limitations associated with conventionalhistopathological diagnostic strategies, novel detection tech-niques based on molecular biomarkers or signature proteinpatterns are coming at the forefront for brain tumor diagno-sis [8]. In the recent years, considerable effort has been madefor the identification and establishment of single marker orsignature of early diagnostic markers for GBM in differentbiological samples [9–11]. To this end, serum is an attractivebiological fluid due to easy accessibility, presence of diver-sity of proteins released by diseased tissues, and its ability toenvisage an inclusive representation of the pathophysiologi-cal condition of a patient [12]. Over the last decade proteomelevel studies have facilitated the identification of several po-tential serological markers for GBM [13], although promis-ing, hitherto, none of the identified candidates has been suc-cessfully translated into direct clinical diagnostics, indicatingthe need of further comprehensive studies to identify thenovel surrogate protein markers, which can aid in monitor-ing disease progression and following prognosis in responseto the therapeutic interventions. In the present study, wehave performed a comparative analysis of serum proteome ofGBM patients and healthy subjects to study alteration in hu-man serum proteome in glioblastoma and for the identifica-tion of potential protein markers. Using different proteomicapproaches, we identified multiple differentially expressedserum proteins, a few of that could further be investigatedas potential surrogate markers for GBM. Possible associationof the identified proteins in essential physiological pathways

was also examined as a component of this comprehensiveanalysis.

2 Materials and methods

2.1 Patient selection and study design

Serum samples were collected from patients (n = 40) withradiologically suspected gliomas undergoing surgery at theAdvanced Center for Treatment Research and Educationin Cancer (ACTREC), and Tata Memorial Hospital (TMH),Mumbai, India (Supporting Information Table S1). The studywas approved by the TMC-ACTREC- Institutional ReviewBoard and all samples were collected after obtaining priorwritten informed consent. For comparative proteomic anal-ysis, serum samples were also collected from healthy sub-jects (n = 40) devoid of any past history of head injury orcharacteristic symptoms for an intracerebral lesion. Controlsamples were obtained from the Departments of ClinicalPharmacology, Seth GS Medical College and KEM Hospital,Parel, Mumbai, with the approval from the Hospital EthicsCommittee and written informed consent from each healthysubject.

2.2 Processing of serum samples, 2DE and 2D-DIGE

Sample processing, 2DE and 2D-DIGE were performed asdescribed previously [14]. In brief, after processing, serumproteins were loaded on 4–7 pH range IPG strips and iso-electric focusing was performed for overall approximately78 kVh. The second dimension was performed on 12.5%SDS polyacrylamide gels (see Supporting Information Mate-rials and Methods for details).

2.3 Image acquisition and software analysis

Image acquisition and data analysis were executed as de-scribed previously [14]. Briefly, comparative analysis for rela-tive protein quantification across the GBM and healthy con-trol (HC) samples was carried out using differential in-gelanalysis (DIA) and biological variation analysis (BVA) mod-ules of DeCyder software, version 7.0 (GE Healthcare, SanFrancisco, CA, USA) (see Supporting Information Materialsand Methods for details).

2.4 In-gel trypsin digestion and mass spectrometry

The identity of differentially expressed proteins (p <0.05)was established using AB Sciex 4800 MALDI-TOF/TOF massspectrometer. In-gel digestion was done as mentioned in [15]and mass spectrometric analysis was performed as describedpreviously [14].


2380 K. Gollapalli et al. Proteomics 2012, 12, 2378–2390

2.5 Immunoturbidimetric assay and Western blot

analysis

The quantitative immunological determination of hap-toglobin in serum samples of HC and GBM patients wasperformed by immunoturbidimetric assay using COBAS IN-TEGRA 400 PLUS system (Roche Diagnostics, Rotkreuz,Switzerland). Western blotting was performed by using mon-oclonal/polyclonal antibodies against haptoglobin, cerulo-plasmin, plasma retinol binding protein and hemopexin, andappropriate secondary antibody conjugated with HRP (seeSupporting Information Materials and Methods for details).

2.6 Biochemical tests for lipid measurements

The serum lipid concentrations of the GBM patients andhealthy subjects were measured by using the Siemens Di-mension Xpand Plus instrument in an automated mannerusing Flex reagent cartridge following the manufacturer’s in-structions (Siemens Healthcare Diagnostics) (see SupportingInformation Materials and Methods for details).

2.7 Protein networks and functional analysis

Differentially expressed serum proteins in GBM were sub-jected to functional pathway analysis using ingenuity path-way analysis (IPA) version 9.0, protein analysis through evo-lutionary relationships (PANTHER) system version 7 [16],and database for annotation, visualization and integrated dis-covery (DAVID) version 6.7 [17]. To determine the biologicalpathways with significant enrichment of the input proteins,algorithms in GeneSpring software package (version 11.5,Agilent Technologies, Santa Clara, CA, USA) was used (seeSupporting Information Materials and Methods for details).

2.8 Multivariate statistical analysis and

development of statistical classifier

To select the most characteristic marker proteins for efficientdiscrimination between GBM and HC, a subset of the iden-tified proteins (Supporting Information Table S9.1 and S9.4)was used to develop a statistical classifier. We used partialleast squares discriminant analysis (PLS-DA), support vectormachine (SVM), Decision Trees (DT), and Naıve Bayes (NB)implemented in Mass Profiler Professional (MPP) softwarepackage (version 2.2, Agilent Technologies) for all multivari-ate and machine learning analysis (see Supporting Informa-tion Materials and Methods for details).

3 Results

3.1 Identification of differentially expressed proteins

in glioblstoma

In order to identify the differentially expressed proteins inthe GBM patients as compared to the healthy subjects, wehave carried out comparative proteomic analysis. Two levelsof gel-based proteomic analysis using regular 2DE and ad-vanced 2D-DIGE techniques were performed. Total 20 GBMpatients serum proteome was compared with 20 healthysubjects serum proteome (n = 20) by using classical 2DE.A subset of the subjects, GBM patients, and healthy con-trols (n = 8 each) were selected for the 2D-DIGE experiment.We used tight statistical filtering during the selection of thedifferentially expressed proteins and only those spots that sat-isfied the criteria (t-test and one-way ANOVA; p <0.05) wereselected for further mass spectrometric and functional analy-sis. In 2DE profiling, we identified ten statistically significant(p ≤0.05) differentially expressed (with changes from –6.58-fold to +3.42-fold) protein spots (Supporting Information Fig.S1 and Table S2). IMP7 software detected over 700 proteinspots reproducibly in each gel stained with GelCode BlueSafe Protein Stain. Representative 2DE images of serum pro-teome profile of GBM patients and healthy individuals andbar-diagrammatic representation of the fold change and 3Dviews of differentially expressed proteins are depicted in Fig.1A and B. MS and MS/MS analysis results revealed that theseten spots represent a total of eight proteins, among that fivewere upregulated and the remaining three were downreg-ulated (Fig. 1, Table 1 and Supporting Information TableS4.1).

The DeCyder 2D software analysis detected approximately1300 protein spots on each 2D-DIGE gels. In 2D-DIGE anal-ysis, a total of 136 (around 10.4% of the entire detected spots)differentially expressed spots satisfied the statistical param-eters (t-test and one-way ANOVA; p <0.05), among which54 exhibited decrease in expression level (range from 1.27-to 4.47-fold) while the remaining 82 protein spots were up-regulated (with changes from 1.21- to 4.53-fold) in the GBMpatients (Supporting Information Table S3). Among the 82upregulated spots, 71 spots were between 1.1- and 2-fold, tenspots were between two- and four-fold, and one spot exhibitedover four-fold increase in expression level; while in the caseof downregulation, 44 spots were between 1.1- and 2-fold,nine spots were between two- and four-fold, and one spotexhibited over four-fold reduced expression level. Among the136 differentially expressed protein spots, 81 spots that couldbe excised from the gels were further subjected to MS andMS/MS analysis. The identity of 55 spots was establishedfrom the MALDI-TOF/TOF MS analysis of trypsin-digestedpeptides (Fig. 2A and Supporting Information Table S4.2).We repeated the MS analysis of those spots, which could notbe identified first time, but unable to establish MS identityeven second time most likely due to the very low intensity ofthose spots and insufficient amount of detectable peptides,


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Figure 1. Trends of differentially expressed proteins in glioblastoma patients visualized on 2DE gels. (A) 2DE serum profile of healthycontrols and GBM patients. Representative 2D gels of serum from healthy controls and GBM patients containing 600 �g depleted serumproteins. Protein samples were focused on linear pH 4–7 IPG strips (18 cm) and then separated on 12.5% polyacrylamide gels, which werestained with Gel Code Blue Stain. (B) The 3D images of statistically significant (p <0.05) differentially expressed serum proteins identifiedin GBM patients. Data are represented as mean ± SE (where n = 20).



Table 1. List of significant differentially expressed proteins identified in the serum of GBM patients using 2DE and 2D-DIGE

S.No. Name of protein with UniProt accession number Fold change p-value Molecularfunctionsa)

Down-regulated proteins

1 (P01871) Ig mu chain C regionb) 1.36 – 1.68 0.0006 – 0.043 N2 (P02790) Hemopexin precursor (Beta-1B-glycoprotein)b) 1.57 – 2.13 0.008 – 0.02 A,C,D3 (P02787) Serotransferrin precursorb) 1.34 – 1.54 0.02 – 0.043 A,C4 (P02768) Serum albumin precursorc) 1.31 – 3.52 0.02 – 0.0025 B,D,E,P,Q5 (P01028) Complement C4 precursorb) 1.73 – 2.18 0.0061 – 0.028 A,L6 (P01024) Complement C3 precursorc) 1.98 – 6.59 2.15 × 10−06–

4.70 × 10−06J,L

7 (P02647) Apolipoprotein A1 precursord) 1.86 0.00041 A,H,IUp-regulated proteins

8 (P01011) Alpha-1-antichymotrypsin precursorb) 1.29 – 1.36 0.013 – 0.022 A,B,F9 (Q9BWW8) Apolipoprotein L6b) 1.65 0.0017 H,I10 (Q14624) Inter-alpha-trypsin inhibitor heavy chain H4

precursorc)1.25 – 3.18 0.034 – 0.0003 F

11 (P10909) Clusterin precursorb) 1.41 – 1.64 0.0048 – 0.025 A,O12 (P06727) Apolipoprotein A-IV precursorb) 1.41 0.014 A,C,D,H,I13 (P02766) Transthyretin precursorb) 1.77 – 1.85 0.0074 – 0.023 A,R14 (P09871) Complement C1s subcomponent precursorb) 1.46 – 1.72 2.4 × 10−04 –

1.6 E−03C,F

15 (P04217) Alpha-1B-glycoprotein precursorb) 1.41 0.0083 – 0.013 S16 (P02774) Vit-D binding protein precursorb) 1.47 0.0051 K17 (P02760) AMBP protein precursorc) 1.49 – 2.29 0.002 – 0.041 F,N18 (P02748) Complement component C9 precursorb) 1.32 0.019 L,M,O19 (P02743) Serum amyloid P-component precursorb) 1.85 0.0037 A,C,G20 (P01876) Ig alpha-1 chain C regionc) 1.32 – 3.4 0.008 – 0.048 N21 (P01834) Ig kappa chain C regionb) 1.74 0.0053 N22 (P01009) Alpha-1-antitrypsin precursorc) 1.43 – 1.88 0.038 – 0.018 A,F23 (P01008) Antithrombin-III precursorb) 1.31 0.012 A,F,O24 (P00747) Plasminogen precursorb) 1.21 0.048 A,H,F,P25 (P00738) Haptoglobin precursorc) 1.22 – 1.80 0.033 – 0.016 A,F,O26 (P00450) Ceruloplasmin precursorb) 1.29 – 1.77 0.006 – 0.049 C,D,Q27 (O95445) Apolipoprotein Mb) 1.64 0.00048 H,I28 (P61641) Plasma retinol-binding protein precursorb) 1.85 0.0012 A,K

a) Revealed by GeneSpring version 11.5 software analysis. A: protein binding; B: DNA binding; C: metal binding; D: antioxidant activity;E: drug binding; F: peptidase/protease inhibitor activity; G: carbohydrate/sugar binding; H: lipid binding; I: lipid metabolism/transport; J:brain development; K: vitamin binding/transport; L: complement activation; M: blood coagulation; N: antigen/antibody binding; O: catalyticactivity; P: cell surface binding; Q: chaperone binding; R: hormone activity/binding; S: Plasma glyco protein, function unknown.b) Proteins identified in 2D-DIGE experiment onlyc) Proteins identified in both 2DE and 2D-DIGE experimentsd) Proteins identified in 2DE experiment only

probably beyond the sensitivity limit of the instrument. The55 protein spots identified by MS correspond to 27 (six down-regulated and 21 upregulated) differentially expressed pro-teins in GBM patients (Table 1; Supporting Information Fig.S2). The 3D views and graphical representation of few se-lected protein spots are shown in Fig. 2B. Of the eight pro-teins identified in 2DE, seven were common in 2DE and2D-DIGE, showing the similar trend of differential expres-sion. Owing to the superior sensitivity and reproducibilityissues, we obtained much higher numbers of differentiallyexpressed protein spots from 2D-DIGE experiment.

3.2 Validation of the identified differentially

expressed proteins

Validation of few selected differentially expressed proteinswas performed using different immunoassay-based tech-niques including immunoturbidimetric assay and Westernblotting to substantiate the results of proteomic analysis. Val-idation of four differentially expressed proteins, haptoglobin,hemopexin, ceruloplasmin, and plasma retinol binding pro-tein was performed using Western blot analysis employ-ing the control and cancerous samples previously analyzed


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Figure 2. Trend of differentially expressed proteins in GBM identified using 2D-DIGE. (A) Representative 2D-DIGE image to compare serumproteome of HC and GBM patients. GBM and HC samples were labeled with Cy3 and Cy5 respectively, while the protein reference pool(internal standard) was labeled with Cy2. (B) Graphical and 3D fluorescence intensity representations of few selected statistically significant(p <0.05) differentially expressed proteins in GBM patients identified in biological variation analysis (BVA) using DeCyder 2D software.Decrease/increase in the standardized log abundance of spot intensity in GBM patient cohort of the study, has been represented graphically(n = 8).

using 2DE and 2D-DIGE. Additionally, a new cohort con-taining 20 of healthy controls and GBM patients each wereincluded for blind validation. Equal loading of the samples(GBM and HC) was verified by CBB staining of the SDS-PAGE gels and Ponceau staining of the transferred blotscontaining the resolved proteins (Supporting InformationFig. S3A and B). Selection of the proteins for validation wasdone on the basis of fold changes of the proteins, possiblefunctional association of the proteins with GBM pathobiol-ogy and availability of the antibodies. Increased expressionlevel of haptoglobin (1.22- to 1.8-fold), ceruloplasmin (1.29-to 1.77-fold), and plasma retinol binding protein (1.85-fold),and downregulation of hemopexin (1.57- to 2.13-fold) wasidentified in GBM patients in 2DE and 2D-DIGE analysis(Table 1). Western blot analysis also revealed upregulation of

haptoglobin (1.1-fold, p = 0.537), ceruloplasmin (1.36-fold,p = 0.023), and plasma retinol binding protein (1.45-fold, p= 0.0002), and downregulation of hemopexin (1.31-fold, p =0.0096) in the GBM patients compared to the healthy subjects(Fig. 3A), which confirmed the results obtained from 2DE and2D-DIGE analysis. Direct immunoturbidimetric measure-ment of the haptoglobin was performed in the serum samplesof GBM patients and healthy subjects (n = 20) that were pre-viously analyzed in 2DE. Compared to the healthy controls,GBM patients exhibited around two-fold higher serum levelof haptoglobin (p <0.001 in a Mann–Whitney test) (Fig. 3B).The mean haptoglobin concentration in GBM patients wasfound to be 1.792 ± 0.17 g/L, whereas the healthy subjectsshowed a mean value of 0.918 ± 0.1 g/L (mean ± SE) (Sup-porting Information Table S5).



Figure 3. Immunoassay-based validation of differentially expressed proteins and biochemical measurements of lipids and lipoproteins inGBM patients. (A) Western blot analysis of hemopexin, haptoglobin, ceruloplasmin, and plasma retinal binding protein from the serumsamples of GBM patients (n = 40) and healthy subjects (n = 40). Validation of the target proteins was performed employing the subjects(n = 20) analyzed by 2-DE/ 2D-DIGE as well as a newly included cohort of equal population size (n = 20). Blots of the target proteins arerepresented along with their respective abundance volumes using dot plots. Western blot analysis indicates upregulation of haptoglobin,ceruloplasmin, and plasma retinol binding protein and reduced expression of hemopexin in the GBM patients. (B) Immunoturbidimetricmeasurement of serum haptoglobin levels in healthy subjects (n = 20) and GBM patients (n = 20). Dot plots depicting the haptoglobinconcentrations (g/L) determined by immunoturbidimetry indicate that the GBM patients have around two-fold higher serum level ofhaptoglobin compared to the controls (p <0.001; Mann–Whitney test). (C) Biochemical measurements of CHOL (cholesterol), TGL (triglyc-erides), HDL (high-density lipoproteins), and LDL (low-density lipoproteins) in healthy subjects (n = 40) and GBM patients (n = 40). Dotplots depicting the lipid concentrations (mg/dL) indicate that the GBM patients have elevated serum levels of CHOL and LDL cholesterolcompared to the healthy controls (p <0.05) (� indicating the initial cohorts analyzed by gel-based proteomics and � indicating the newlyincluded populations used for blind validation).


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3.3 Serum levels of lipids in GBM patients and

healthy subjects

The serum levels of cholesterol, triglycerides, LDL choles-terol, and HDL cholesterol were estimated in GBM patientsand healthy subjects by using different automated enzymaticassays (n = 40 each) (Supporting Information Table S6).Elevated serum levels of lipids were observed in the GBMpatients (Fig. 3C). The mean concentration of cholesterolwas found to be 173.58 ± 6.389 mg/dL in GBM patients,whereas it was 132.55 ± 5.101 mg/dL for the controlgroup (data are presented as mean ± SE) (p = 3.4591 ×10−06). The mean concentration of the LDL cholesterolin the serum of the GBM patients and healthy subjectswere found to be 113.5 ± 4.963 mg/dL and 82.8 ± 3.95mg/dL, respectively, (p = 6.88044 × 10−06). The serum HDLcholesterol levels also increased in the GBM patients (39.9 ±2.019 mg/dL) when compared to the healthy subjects (33.5± 1.472 mg/dL) (p = 1.255 × 10−02). The serum triglycerideslevels increased in the GBM patients (147.625 ± 9.469mg/dL) compared to the healthy subjects (126 ± 11.384mg/dL) but it was statistically insignificant (p = 0.1483;Fig. 3C).

3.4 Interaction networks and functional pathway

analysis

Functional pathway analysis was performed with the differ-entially expressed serum proteins identified in the GBM pa-tients for better understanding of their biological context,involvement in diverse physiological pathways, and associa-tion with glioma tumor growth, progression, and migration.Gene ontology (GO) analysis allowed us to elucidate the dif-ferent functions and process in which the 28 proteins areputatively involved. The molecular function (Fig. 4A), cel-lular component (Fig. 4B), and biological process of the 28proteins are presented (Supporting Information Table S7).According to the molecular function analysis (Fig. 4A), mostof the proteins were related with binding (48%), enzyme reg-ulation and catalytic activity (26%), and transport (20%). Asmall fraction (6%) is involved in channel regulation andantioxidant activity. Most of the proteins were found in theextracellular (40%) or cell region (34%) as shown in Fig. 4Bby cellular component analysis. Concerning biological pro-cess, the identified proteins were involved in metabolic pro-cess (14%), response to stimulus (14%), biological regulation(14%), cellular process (11%), localization (9%), and immunesystem process (6%) (Supporting Information Table S7 andFig. S4).

In IPA analysis of the 28 query molecules, 27 were el-igible for network analysis (focus molecule) based on theIPA Knowledge Base criteria. Two major overlapping in-teraction networks were identified, where the highest scor-ing network included 13 of the 27 focus molecules witha score 31, while the second network (score 28) included

12 focus molecules (Supporting Information Fig. S5A andB, and Table S8.1). The most significant related functionsderived from these overlapping networks included, lipidmetabolism (13 molecules, p = 2.83 × 10−07–1.14 × 10−02),molecular transport (16 molecules, p = 2.83 × 10−07–1.05 × 10−02), small molecule biochemistry (17 molecules,p = 2.83 × 10–07–1.14 × 10–02), antigen presentation (13molecules, p = 7.69 × 10−10–1.14 × 10−02), and posttrans-lational modification (seven molecules, p = 1.00 × 10−07–1.14 × 10−02). Majority of the identified differentially ex-pressed proteins found to be related to cancer diseases (20molecules, p = 1.28 × 10−11–1.25 × 10−02) and inflammatoryresponse (18 molecules, p = 2.70 × 10−11–1.14 × 10−02). Ac-cording to this IPA functional analysis, glioma tumor growthand progression lead to the alteration of multiple serum pro-teins involved in diverse essential physiological pathways,including acute phase response (ratio = 0.079, p = 1.48 ×10−22), complement cascade (ratio = 0.114, p = 1.47 × 10−07),and coagulation system (ratio = 0.079, p = 1.64 × 10−05) (Sup-porting Information Table S8.2). PANTHER analysis also re-vealed the involvement of the identified proteins in the bloodcoagulation system (p = 3.1 × 10−05), vitamin-D metabolism(p = 1.74 × 10−02), and plasminogen-activating cascade (p =2.24 × 10−02) (Supporting Information Table S8.3). Likewise,complement and coagulation cascades (p = 3.47 × 10−11) andsignaling in immune system (p = 1.41 × 10−02) were identi-fied in DAVID analysis (Supporting Information Table S8.4and Fig. S5C).

3.5 Discrimination of GBM and HC using

multivariate statistical analysis

We applied proteomics data obtained from 2DE analy-sis to discriminate GBM and healthy subjects (HC) us-ing multivariate statistical analysis. Class prediction mod-els were created to independently predict (assign) the phe-notypic class to either GBM or HC group. The PLS-DAmodel provided, 100% accurate phenotypic classificationof GBM (n = 14) and 85.7% of HC (n = 14) (Support-ing Information Table S9.3). We achieved 92.85% over-all prediction accuracy on independent blinded prediction(n = 14) using PLS-DA. For the final validation phase, we com-pared the performance of the biomarker subset using threewell-known machine-learning methods: DT, NB, and SVM.Supporting Information Table S9.3 summarizes the percent-age of the samples classified during model training, cross-validation, and independent prediction, respectively, usingthe three different classifiers. We achieved 92.85 % overallprediction accuracy with SVM and DT followed by NB (85.7%)on blinded prediction (n = 14) using the biomarker data setfor GBM and HC.

Further, 19 differentially expressed proteins (SupportingInformation Table S9.4) identified in DIGE were impli-cated as potential classifiers for the discrimination of GBMand healthy subjects employing similar type of analysis



Figure 4. Gene ontology (GO) terms for molecular functions, cellular components, and association of different essential physiologicalpathways with differentially expressed proteins in GBM. A total of 887 GO terms were identified, of which the distribution of second levelof GO terms that were enriched in two or more proteins is shown as (A) molecular functions and (B) cellular components. (C) Membersof multiple essential physiological processes including cell growth and proliferation, vitamin D metabolism, lipoprotein metabolism andtransport, oxidative stress regulation, complement cascade, and platelet activation found to be modulated in the GBM patients. Differentialexpression of the serum proteins in GBM patients identified in our study has been depicted (upregulated in red, downregulated in green).


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(Supporting Information Fig. S6B). We achieved 87.50% pre-diction accuracy on blinded prediction (n = 8) using PLS-DA.Supporting Information Table S9.6 summarizes the percent-age of the samples classified during model training, cross-validation, and independent prediction, respectively, and con-fidence measure in the final validation phase using the threewell-known machine-learning methods. We achieved 93.75%prediction accuracy independently with each of the threemethods, i.e. SVM, DT, and NB on blinded prediction (n =8) using the biomarker dataset for GBM and HC (SupportingInformation Table S9.6).

4 Discussion

Over the last few years, proteomics has contributed signifi-cantly in gliomas research to investigate disease pathobiologyand identification of novel therapeutic targets and potentialmarkers for GBM and other grades of astrocytoma [18–23].Many of our identified proteins such as haptoglobin, comple-ment C4 precursor, plasminogen precursor, apolipoproteinA1, and associated pathways such as plasminogen activat-ing cascade, vitamin D metabolism, phosphatidylinositol 3-kinase (PI3K) signaling, and intrinsic prothrombin activationpathway have been reported earlier in the context of glioma,which supports our findings and enhances confidence ofthis study, while a few of the identified targets includingceruloplasmin, vitamin D binding protein, serum amyloid P,though not reported directly in context of GBM but have goodcorrelation with tumor growth and metastasis on the basis oftheir known biological functions.

In this study, we have identified elevated serum level ofplasminogen and antithrombin-III, which are associated withplasminogen activating cascade, hemostasis, and coagulationsystem in GBM (Fig. 4C). Thrombin plays an imperative rolein glioma growth and angiogenesis and can affect the growthand proliferation of different types of solid tumors, whichdepends on formation of new blood vessels [24]. Antithrom-bins are glycoproteins that inactivate different enzymes ofthe coagulation system. Various antithrombins – such asargatroban, serpin antithrombin – can affect the gliomagrowth, reduce tumor mass through antiangiogenic activity,and could lengthen the survival time of the glioma patients[25, 26]. Elevated level of antithrombin-III may play crucialrole in controlling the tumor growth in GBM patients throughits antiangiogenic anticoagulation activity. Plasminogen acti-vating cascade leads to the conversion of inactive plasmino-gen to plasmins that degrades the basement membrane of thetumor tissues leading to metastasis [27] and contribute to col-lagen cleavage by activation of latent matrix metalloproteases[28]. Possible involvement of plasminogen activation cascadein glial tumorigenesis has been demonstrated earlier [29]. Incase of GBM, the increased levels of plasminogen in serumcould cause the destruction of matrix leading to the enhancedcellular motility and metastasis since blood brain barrier getsdisrupted in GBM. In our proteomic study, we also identified

the deregulation of the PI3K signaling cascade in GBM pa-tients, which is consistent with earlier reports [30]. Activationof intracellular signaling pathways such as PI3K/Akt can leadto the overexpression of VEGF, which participates in the for-mation and development of blood vessels in glioma tissues[31]. Decrease in serum levels of few complement factors in-cluding complement C3 and C4 precursor, identified in thisstudy, is in agreement with previous reports [32]. Reducedexpression of the complement factors may be a reflection ofthe overall immunosuppression observed in GBM patients.

Increased serum level of multiple acute phase proteins(APPs) including haptoglobin (1.2- to 1.8-fold), ceruloplas-min (1.29- to 1.77-fold), �-1B-glycoprotein (1.41-fold), serumamyloid P (1.85-fold), and plasma retinol binding protein(1.85-fold) (Table 1) in GBM patients have been identifiedin this study. APPs are a group of serum proteins involvedin multiple biological processes including phagocytosis, op-sonization, protein transport, and combating oxygen toxicity.Haptoglobin and ceruloplasmin are very important APPs dueto their vital role associated with glioma pathophysiology. Ourresults show around two-fold upregulation of haptoglobin inthe GBM patients, which is consistent with the earlier re-ports [9, 33]. Although precise role of haptoglobin in GBMpatients is not completely known, it is postulated that hap-toglobin may contribute to the very high proliferative natureof GBMs, since it promotes tumor angiogenesis [9]. Anothercandidate protein, ceruloplasmin, which is copper contain-ing and plays a vital role in oxidation of iron atoms in humanbody showed 1.5-fold upregulation in GBM sera (Table 1).Higher activity of ceruloplasmin is reported in other can-cers and neurodegenerative disorders such as Parkinson’sdisease [34]. In GBM, serum ceruloplasmin levels are foundto be increased in proportion to malignancy of the tumor[35]. Ceruloplasmin acts as an antioxidant and converts thetoxic ferrous ions to ferric ions and thereby protects the braintissue from the oxidative damage [36]. This APP also playsan important role in buffering the serum copper level, whenhigh amount of copper is released into the serum from theglioma tumor tissues, which contain high amount of thismetal [35]. Two serine protease inhibitors, �-1-antitrypsin and�-1-antichymotrypsin – members of SERPINS super fam-ily found to be overexpressed (∼1.5-fold) in GBM patients(Table 1). These glycoproteins are produced by different typesof cells such as hepatocytes, monocytes, macrophages, and tu-mor cells, and can inhibit a number of serine proteases suchas trypsin, chymotrypsin, elastase and reduce tumor metasta-sis. Elevated level of another serine protease inhibitor-inter-�-trypsin inhibitor heavy chain H4 was identified in GBMpatients in our study. Although the exact reason for the up-regulation of these protease inhibitors is not clear, it might bea consequence of host response against the malignant tumorsto reduce their metastasis.

Interestingly, quite a few of the identified serum pro-teins exhibiting altered expression in GBM patients arefound to be associated with lipid metabolism and transport.Lipid metabolism impairment in human gliomas has been



reported previously [37]. Lipid and lipoprotein profiling re-vealed elevated serum levels of total cholesterol, triacylglyc-erol, low-density lipoprotein in GBM patients compared tohealthy subjects (Supporting Information Table S6), which isconsistent with previously reported studies [38]. Assessmentof serum proteins and cholesterol in malignancy has prognos-tic implications [39] and could be employed as a complemen-tary diagnostic tool for the evaluation of brain tumors growthand progression [40]. The GBM patients analyzed in this studyreceived steroid treatment prior to the sample collection thatmight be a cause for elevated serum levels of total choles-terol [41]. However, significantly increased level of choles-terol, low-density lipoprotein-cholesterol (LDL-C) and High-density lipoprotein-cholesterol (HDL-C) was also observed,indicating a correlation of blood lipid and lipoprotein patternswith glioma pathobiology. In contrast, most of the other pe-ripheral malignancies, which lead to tumor-associated hypoc-holesterolemia as a consequence of enhanced cholesterol up-take by proliferating tumor cells [42, 43], increased serumlevel of cholesterol in GBM probably because of the de novocholesterol synthesizing capabilities of human gliomas or theexistence of brain tumor blood barrier [44, 45]. Increased ex-pression of multiple apolipoproteins, including apolipopro-tein L6, A-IV, M, etc. and other candidates involved in lipidmetabolism and transport activities (Table 1) in GBM patientsmight be due to a host response to tackle the elevated serumlipid levels.

Another interesting finding of this study is modulation ofvitamin D metabolism pathway with an upregulation (∼1.5-fold) of vitamin D binding protein (DBP) in the GBM patients(Table 1; Fig. 4C). Earlier reports have also demonstrated theimportant role of the vitamin D metabolism in human GBM[46]. DBP contributes to immune modulation, possess an-tiproliferative and antiangiogenic properties [47], and playsa vital role in metabolism of vitamin D and its transporta-tion. In cancer patients, macrophage activation is found to besuppressed due to the inactivation of glycosylated DBP by a-N-acetylgalactosaminidase, an endoglycosidase produced bythe cancer cells [48]. Vitamin D derivatives can induce celldeath of glioblastoma cells in vitro and exhibit lethal effectson glioblastoma cells under in vivo condition by increasingintracellular ceramide concentration through the activationof the sphingomyelin pathway [49]. Increased expression ofthis multifunctional transporter protein has not been directlyreported earlier in the context of GBM, however, it has beenfound to be overexpressed in other cancers [50]. Increased pro-duction of DBP, in GBM patients’ sera samples, might be use-ful to enhance the amount of circulating form of this proteinto prevent angiogenesis in cancer patients. These findingsmay open up new strategies for vitamin D related therapiesfor glioblastoma.

To evaluate the efficacy of the identified differentially ex-pressed proteins for the discrimination of GBM from healthypopulation, a subset of the proteins identified in our pro-teomic analysis was used to build statistical sample class pre-diction models. Using PLS-DA, we were able to discriminate

between groups representing GBM and healthy individuals(Supporting Information Fig. S6). Applying PLS-DA on pro-teomic data, we could predict GBM and HC individuals with92.85% (n = 14) and 87.5% (n = 8) classification success, us-ing biomarker subset identified by 2DE and 2D-DIGE respec-tively (Supporting Information Table S9). For the validationpurpose, we compared the performance of biomarker sub-set using three other well-known machine-learning methods:DT, NB, and SVM, and obtained nearly 100% accuracy (Sup-porting Information Table S9.3 and S9.6). Using five proteinsidentified in classical 2DE analysis, accurate discriminationbetween the GBM and HC groups was obtained. From diag-nostic point of view, small panel of proteins is more attractiveand feasible; however, additional classifier proteins identi-fied in 2D-DIGE may be required for discriminating GBMfrom other related peripheral malignancies, where only fiveproteins identified in 2DE may not be sufficient for accuratediscrimination.

In summary, we used proteomic approaches to investigatethe alteration in human serum proteome in glioblastoma pa-tients with an intention to study the disease pathobiology. Ourresults have shown that multiple serum proteins associatedwith different vital physiological pathways are differentiallyexpressed in GBM patients. Validation of altered expressionlevel of four potential marker proteins performed on a newlyemployed cohort of GBM patients and healthy subjects (n =20 each) testified the efficacy of this initial screening proce-dure identified using proteomics. Evaluation of the specificityof the identified GBM-related serum proteins by means ofanalysis of patients with various other peripheral malignan-cies will be interesting and could be a future continuation ofthis study. It would also be interesting to further investigatethe fate of the identified serum proteins in different stages ofglioma. Nonetheless, we anticipate that the information ob-tained from this study would enhance the understanding ofunderlying molecular mechanisms of tumor growth and pro-liferations in GBM and may help to provide novel diagnosticsolutions.

The active support from ACTREC and TMH, Mumbai in clin-ical sample collection process is gratefully acknowledged. We wouldlike to acknowledge the Departments of clinical pharmacology,Seth GS Medical College and KEM Hospital, Mumbai for provid-ing the serum samples from healthy subjects for this comparativeproteomic analysis. We would also like to thank Dr. ShantanuSengupta and Gaurav Garg, IGIB, Delhi for the help in perform-ing immunoturbidimetric assays, and Dr. Geetanjali Sachdevaand Sumit Bhutada, NIRRH, Parel, Mumbai for the support inexecuting scanning of the 2D-DIGE gels. The help rendered byKarthik S. Kamath and in 2D-DIGE experiment and Shipra V.Gupta in functional pathway analysis is gratefully acknowledged.This research was supported by Department of Biotechnology, In-dia grant (No. BT/PR14359/MED/30/916/2010) and a start-up grant 09IRCC007 from the IIT Bombay to S.S. K.G. and S.R.are supported by the IIT Bombay fellowship.

The authors have declared no conflict of interest.


Proteomics 2012, 12, 2378–2390 2389

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investigation of serum proteome alterations in human glioblastoma multiforme

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