Serum proteomics of glioma: methods and applications

Predictive diagnostic and/or prognostic biomarkers obtained from routine blood biochemistry in patients with solitary intracranial tumor

Background

Radiological and/or laboratory tests may be sometimes inadequate distinguishing glioblastoma from metastatic brain tumors. The aim of this study was to find possible predictive biomarkers produced from routine blood biochemistry analysis results evaluated preoperatively in each patient with solitary brain tumor in distinguishing glioblastoma from metastatic brain tumors as well as revealing short-term prognosis.

Methods

Patients admitted to neurosurgery clinic between January 2015 and September 2018 were included in this study and they were divided into GLIOMA (n=12) and METASTASIS (n=17) groups. Patients' data consisted of age, gender, Glasgow Coma Scale scores, duration of stay in hospital, Glasgow Outcome Scale (GOS) scores and histopathological examination reports, hemoglobin level, leukocyte, neutrophil, lymphocyte, monocyte, eosinophil, basophil and platelet count results, neutrophil-lymphocyte ratio and platelet-lymphocyte ratio values, C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) levels were evaluated preoperatively.

Results

The CRP levels of METASTASIS group (143.10 mg/L) were higher than those of GLIOMA group (23.90 mg/L); and it was 82% sensitive and 75% specific in distinguishing metastatic brain tumor from glioblastoma if CRP value was >55.00 mg/L. A positive correlation was determined between GOS score and hemoglobin level and between ESR and CRP values. However, GOS scores were negatively correlated with the ESR level and duration of stay in hospital.

Conclusions

Study results demonstrated that CRP values could be predictive biomarker in distinguishing metastatic brain tumor from glioblastoma. In addition, ESR, CRP, hemoglobin levels and duration of stay in hospital could be prognostic biomarkers in predicting short-term prognosis of patients with solitary brain tumor.

The development and clinical applications of proteomics: an Indian perspective

INTRODUCTION

Proteomic research has been extensively used to identify potential biomarkers or targets for various diseases. Advances in mass spectrometry along with data analytics have led proteomics to become a powerful tool for exploring the critical molecular players associated with diseases, thereby, playing a significant role in the development of proteomic applications for the clinic.

AREAS COVERED

This review presents recent advances in the development and clinical applications of proteomics in India toward understanding various diseases including cancer, metabolic diseases, and reproductive diseases. Keywords combined with 'clinical proteomics in India' 'proteomic research in India' and 'mass spectrometry' were used to search PubMed.

EXPERT OPINION

The past decade has seen a significant increase in research in clinical proteomics in India. This approach has resulted in the development of proteomics-based marker technologies for disease management in the country. The majority of these investigations are still in the discovery phase and efforts have to be made to address the intended clinical use so that the identified potential biomarkers reach the clinic. To move toward this necessity, there is a pressing need to establish some key infrastructure requirements and meaningful collaborations between the clinicians and scientists which will enable more effective solutions to address health issues specific to India.

Proteomic Advances in Glial Tumors through Mass Spectrometry Approaches

Being the fourth leading cause of cancer-related death, glial tumors are highly diverse tumor entities characterized by important heterogeneity regarding tumor malignancy and prognosis. However, despite the identification of important alterations in the genome of the glial tumors, there remains a gap in understanding the mechanisms involved in glioma malignancy. Previous research focused on decoding the genomic alterations in these tumors, but due to intricate cellular mechanisms, the genomic findings do not correlate with the functional proteins expressed at the cellular level. The development of mass spectrometry (MS) based proteomics allowed researchers to study proteins expressed at the cellular level or in serum that may provide new insights on the proteins involved in the proliferation, invasiveness, metastasis and resistance to therapy in glial tumors. The integration of data provided by genomic and proteomic approaches into clinical practice could allow for the identification of new predictive, diagnostic and prognostic biomarkers that will improve the clinical management of patients with glial tumors. This paper aims to provide an updated review of the recent proteomic findings, possible clinical applications, and future research perspectives in diffuse astrocytic and oligodendroglial tumors, pilocytic astrocytomas, and ependymomas.

Blood-Based Biomarkers in High Grade Gliomas: a Systematic Review

High-grade gliomas (HGG) are the most common malignant primary brain tumor in adults. During the course of disease, several challenges occur, like measuring tumor burden, monitoring of treatment response, estimating the patient's prognosis, and distinguishing between true progression and pseudo-progression. So far, no blood-based biomarker has been established in the clinical routine to address these challenges. The aim of this systematic review was to analyze the present evidence on blood-based biomarkers for HGG. We systematically searched in PubMed, Web of Sciences, Scopus, and Cochrane Library databases for publications before 30th of March 2018 reporting on associations of blood-based biomarkers in HGG patients with different endpoints as overall survival, progression-free survival, and postoperative monitoring. Quality assessment of the studies according to QUIPS and STARD guidelines was performed. In accordance with the GRADE guidelines, level of evidence (I-IV) for each of the tested biomarkers was assessed. One thousand six hundred eighty unique records were identified. Of these, 170 original articles were included to this review. Four hundred fifteen different blood-based biomarkers analyzed in 15.041 patients with HGG as also their corresponding recurrent tumors. Ten predictive biomarkers reached level II of evidence. No biomarker achieved level I of evidence. In this review, 10 blood-based biomarkers were selected as most promising biomarkers for HGG: α2-Heremans-Schmid glycoprotein (AHSG), albumin, glucose, insulin-like growth factor- binding protein 2 (IGFBP-2), macrophage inflammatory protein 1δ (MIP-1 δ), macrophage inflammatory protein 3ß (MIP-3ß), neutrophil-lymphocyte ratio (NLR), red blood cell distribution width (RDW), soluble glycoprotein 130 (Sgp130), and chitinase-3-like protein 1 (YKL-40). To further assess the clinical significance of these biomarkers, the evaluation in a larger cohort of HGG and their corresponding subgroups would be necessary.

Application of 2D-DIGE and iTRAQ Workflows to Analyze CSF in Gliomas

Proteomics is an indispensable tool for disease biomarker discovery. It is widely used for the analysis of biological fluids such as cerebrospinal fluid (CSF), blood, and saliva, which further aids in our understanding of disease incidence and progression. CSF is often the biospecimen of choice in case of intracranial tumors, as rapid changes in the tumor microenvironment can be easily assessed due to its close proximity to the brain. On the contrary studies comprising of serum or plasma samples do not truly reflect the underlying molecular alterations due to the presence of protective blood-brain barrier. We have described in here the detailed workflows for two advanced proteomics techniques, namely, 2D-DIGE (two-dimensional difference in-gel electrophoresis) and iTRAQ (isobaric tag for relative and absolute quantitation), for CSF analysis. Both of these techniques are very sensitive and widely used for quantitative proteomics analysis.

Proteomic analysis of rat serum revealed the effects of chronic sleep deprivation on metabolic, cardiovascular and nervous system

Sleep is an essential and fundamental physiological process that plays crucial roles in the balance of psychological and physical health. Sleep disorder may lead to adverse health outcomes. The effects of sleep deprivation were extensively studied, but its mechanism is still not fully understood. The present study aimed to identify the alterations of serum proteins associated with chronic sleep deprivation, and to seek for potential biomarkers of sleep disorder mediated diseases. A label-free quantitative proteomics technology was used to survey the global changes of serum proteins between normal rats and chronic sleep deprivation rats. A total of 309 proteins were detected in the serum samples and among them, 117 proteins showed more than 1.8-folds abundance alterations between the two groups. Functional enrichment and network analyses of the differential proteins revealed a close relationship between chronic sleep deprivation and several biological processes including energy metabolism, cardiovascular function and nervous function. And four proteins including pyruvate kinase M1, clusterin, kininogen1 and profilin-1were identified as potential biomarkers for chronic sleep deprivation. The four candidates were validated via parallel reaction monitoring (PRM) based targeted proteomics. In addition, protein expression alteration of the four proteins was confirmed in myocardium and brain of rat model. In summary, the comprehensive proteomic study revealed the biological impacts of chronic sleep deprivation and discovered several potential biomarkers. This study provides further insight into the pathological and molecular mechanisms underlying sleep disorders at protein level.

Identification of blood biomarkers in glioblastoma by SWATH mass spectrometry and quantitative targeted absolute proteomics

Molecular biomarkers in blood are needed to aid the early diagnosis and clinical assessment of glioblastoma (GBM). Here, in order to identify biomarker candidates in plasma of GBM patients, we performed quantitative comparisons of the plasma proteomes of GBM patients (n = 14) and healthy controls (n = 15) using SWATH mass spectrometry analysis. The results were validated by means of quantitative targeted absolute proteomics analysis. As a result, we identified eight biomarker candidates for GBM (leucine-rich alpha-2-glycoprotein (LRG1), complement component C9 (C9), C-reactive protein (CRP), alpha-1-antichymotrypsin (SERPINA3), apolipoprotein B-100 (APOB), gelsolin (GSN), Ig alpha-1 chain C region (IGHA1), and apolipoprotein A-IV (APOA4)). Among them, LRG1, C9, CRP, GSN, IGHA1, and APOA4 gave values of the area under the receiver operating characteristics curve of greater than 0.80. To investigate the relationships between the biomarker candidates and GBM biology, we examined correlations between plasma concentrations of biomarker candidates and clinical presentation (tumor size, progression-free survival time, or overall survival time) in GBM patients. The plasma concentrations of LRG1, CRP, and C9 showed significant positive correlations with tumor size (R² = 0.534, 0.495, and 0.452, respectively).

Protein expression in dairy cows with and without subclinical hypocalcaemia

AIM

To determine differences in plasma proteomic profiles between healthy cows and those with subclinical hypocalcaemia within 12 hours after calving, and thereby explore the underlying biological mechanism of subclinical hypocalcaemia in dairy cows.

METHODS

Plasma samples were collected within 6 hours of calving from Holstein cows on a farm in Heilongjiang, China; 32 with subclinical hypocalcaemia (plasma calcium concentration 1.38-2.00 mmol/L and no clinical signs) and 59 control cows (plasma calcium concentration 2.10-2.8 mmol/L). Plasma samples were applied to weak cationic exchange protein chips for protein profiling by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF-MS), and the data were analysed using the PBS-IIC system. The amplitude of peaks for the two groups were compared using the Wilcoxon sum-rank test, and the mass-to-charge ratio of the peaks that differed was used to identify peptide fragments using the Swiss-Prot protein database.

RESULTS

Seven peaks were identified in the subclinical hypocalcaemia group that differed from those of the control group (p<0.001), that represented six unique proteins. Expression of serum albumin, fibrinogen alpha chain, amyloid beta A4 proteins and neurosecretory protein VGF were increased, and expression of apolipoprotein A-II and serum amyloid A proteins were decreased in the subclinical hypocalcaemic cows compared with control cows.

CONCLUSION

Use of SELDI-TOF-MS technology can effectively identify differences in plasma protein expression patterns in cows with subclinical hypocalcaemia. Neurosecretory protein VGF and amyloid beta A4 protein might represent useful biomarkers for diagnosis of subclinical hypocalcaemia.

An Eighteen Serum Cytokine Signature for Discriminating Glioma from Normal Healthy Individuals

Glioblastomas (GBM) are largely incurable as they diffusely infiltrate adjacent brain tissues and are difficult to diagnose at early stages. Biomarkers derived from serum, which can be obtained by minimally invasive procedures, may help in early diagnosis, prognosis and treatment monitoring. To develop a serum cytokine signature, we profiled 48 cytokines in sera derived from normal healthy individuals (n = 26) and different grades of glioma patients (n = 194). We divided the normal and grade IV glioma/GBM serum samples randomly into equal sized training and test sets. In the training set, the Prediction Analysis for Microarrays (PAM) identified a panel of 18 cytokines that could discriminate GBM sera from normal sera with maximum accuracy (95.40%) and minimum error (4.60%). The 18-cytokine signature obtained in the training set discriminated GBM sera from normal sera in the test set as well (accuracy 96.55%; error 3.45%). Interestingly, the 18-cytokine signature also differentiated grade II/Diffuse Astrocytoma (DA) and grade III/Anaplastic Astrocytoma (AA) sera from normal sera very efficiently (DA vs. normal–accuracy 96.00%, error 4.00%; AA vs. normal–accuracy 95.83%, error 4.17%). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using 18 cytokines resulted in the enrichment of two pathways, cytokine-cytokine receptor interaction and JAK-STAT pathways with high significance. Thus our study identified an 18-cytokine signature for distinguishing glioma sera from normal healthy individual sera and also demonstrated the importance of their differential abundance in glioma biology.

Definition of a serum marker panel for glioblastoma discrimination and identification of Interleukin 1β in the microglial secretome as a novel mediator of endothelial cell survival induced by C-reactive protein

Glioblastoma (GBM) is the most common malignant adult primary brain tumor. We profiled 724 cancer-associated proteins in sera of healthy individuals (n=27) and GBM (n=28) using antibody microarray. While 69 proteins exhibited differential abundance in GBM sera, a three-marker panel (LYAM1, BHE40 and CRP) could discriminate GBM sera from that of healthy donors with an accuracy of 89.7% and p<0.0001. The high abundance of C-reactive protein (CRP) in GBM sera was confirmed in 264 independent samples. High levels of CRP protein was seen in GBM but without a change in transcript levels suggesting a non-tumoral origin. Glioma-secreted Interleukin 6 (IL6) was found to induce hepatocytes to secrete CRP, involving JAK-STAT pathway. The culture supernatant from CRP-treated microglial cells induced endothelial cell survival under nutrient-deprivation condition involving CRP-FcγRIII signaling cascade. Transcript profiling of CRP-treated microglial cells identified Interleukin 1β (IL1β) present in the microglial secretome as the key mediator of CRP-induced endothelial cell survival. IL1β neutralization by antibody-binding or siRNA-mediated silencing in microglial cells reduced the ability of the supernatant from CRP-treated microglial cells to induce endothelial cell survival. Thus our study identifies a serum based three-marker panel for GBM diagnosis and provides leads for developing targeted therapies. Biological significance A complex antibody microarray based serum marker profiling identified a three-marker panel - LYAM1, BHE40 and CRP as an accurate discriminator of glioblastoma sera from that of healthy individuals. CRP protein is seen in high levels without a concomitant increase of CRP transcripts in glioblastoma. Glioma-secreted IL6 induced hepatocytes to produce CRP in a JAK-STAT signaling dependent manner. CRP induced microglial cells to release IL1β which in turn promoted endothelial cell survival. This study, besides defining a serum panel for glioblastoma discrimination, identified IL1β as a potential candidate for developing targeted therapy.

Proteomics analysis of human brain glial cell proteome by 2D gel

INTRODUCTION

Proteomics is increasingly employed in both neurological and oncological research, and applied widely in every area of neuroscience research including brain cancer. Astrocytomas are the most common glioma and can occur in most parts of the brain and occasionally in the spinal cord. Patients with high-grade astrocytomas have a life expectancy of <1 year even after surgery, chemotherapy, and radiotherapy.

MATERIALS AND METHODS

We extracted proteins from tumors and normal brain tissues and then evaluated the protein purity by Bradford test and spectrophotometry method. In this study, we separated proteins by the two-dimensional (2DG) gel electrophoresis method, and the spots were analyzed and compared using statistical data.

RESULTS

On each analytical 2D gel, an average of 800 spots was observed. In this study, 164 spots exhibited up-regulation of expression level, whereas the remaining 179 spots decreased in astrocytoma tumor relative to normal tissue. RESULTS demonstrate that functional clustering and principal component analysis (PCA) has considerable merits in aiding the interpretation of proteomic data. Proteomics is a powerful tool in identifying multiple proteins that are altered following a neuropharmacological intervention in a disease of the central nervous system (CNS).

CONCLUSION

2-D gel and cluster analysis have important roles in the diagnostic management of astrocytoma patients, providing insight into tumor biology. The application of proteomics to CNS research has invariably been very successful in yielding large amounts of data.

The C-terminal fragment of prostate-specific antigen, a 2331 Da peptide, as a new urinary pathognomonic biomarker candidate for diagnosing prostate cancer

Background and Objectives

Prostate cancer (PCa) is one of the most common cancers and leading cause of cancer-related deaths in men. Mass screening has been carried out since the 1990s using prostate-specific antigen (PSA) levels in the serum as a PCa biomarker. However, although PSA is an excellent organ-specific marker, it is not a cancer-specific marker. Therefore, the aim of this study was to discover new biomarkers for the diagnosis of PCa.

Materials and Methods

We focused on urine samples voided following prostate massage (digital rectal examination [DRE]) and conducted a peptidomic analysis of these samples using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/MSⁿ). Urinary biomaterials were concentrated and desalted using CM-Sepharose prior to the following analyses being performed by MALDI-TOF/MSⁿ: 1) differential analyses of mass spectra; 2) determination of amino acid sequences; and 3) quantitative analyses using a stable isotope-labeled internal standard.

Results

Multivariate analysis of the MALDI-TOF/MS mass spectra of urinary extracts revealed a 2331 Da peptide in urine samples following DRE. This peptide was identified as a C-terminal PSA fragment composed of 19 amino acid residues. Moreover, quantitative analysis of the relationship between isotope-labeled synthetic and intact peptides using MALDI-TOF/MS revealed that this peptide may be a new pathognomonic biomarker candidate that can differentiate PCa patients from non-cancer subjects.

Conclusion

The results of the present study indicate that the 2331 Da peptide fragment of PSA may become a new pathognomonic biomarker for the diagnosis of PCa. A further large-scale investigation is currently underway to assess the possibility of using this peptide in the early detection of PCa.

Towards developing biomarkers for glioblastoma multiforme: a proteomics view

Glioblastoma multiforme (GBM) is one of the most aggressive and lethal forms of the primary brain tumors. With predominance of tumor heterogeneity and emergence of new subtypes, new approaches are needed to develop tissue-based markers for tumor typing or circulatory markers to serve as blood-based assays. Multi-omics data integration for GBM tissues would offer new insights on the molecular view of GBM pathogenesis useful to identify biomarker panels. On the other hand, mapping differentially expressed tissue proteins for their secretory potential through bioinformatics analysis or analysis of the tumor cell secretome or tumor exosomes would enhance our understanding of the tumor microenvironment and prospects for targeting circulatory biomarkers. In this review, the authors first present potential biomarker candidates for GBM that have been reported and then focus on plausible pipelines for multi-omic data integration to identify additional, high-confidence molecular panels for clinical applications in GBM.

Proteomic analysis of cerebrospinal fluid: toward the identification of biomarkers for gliomas

Gliomas are the most common primary brain tumors in adults and, despite advances in the understandings of glioma pathogenesis in the genetic era, they are still ineradicable, justifying the need to develop more reliable diagnostic and prognostic biomarkers for this malignancy. Because changes in cerebrospinal fluid (CSF) are suggested to be capable of sensitively reflecting pathological processes, e.g., neoplastic conditions, in the central nervous system, CSF has been deemed a valuable source for potential biomarkers screening in this era of proteomics. This systematic review focused on the proteomic analysis of glioma CSF that has been published to date and identified a total of 19 differentially expressed proteins. Further functional and protein-protein interaction assessments were performed by using Protein Analysis Through Evolutionary Relationships (PANTHER) website and Ingenuity Pathway Analysis (IPA) software, which revealed several important protein networks (e.g., IL-6/STAT-3) and four novel focus proteins (IL-6, galanin (GAL), HSPA5, and WNT4) that might be involved in glioma pathogenesis. The concentrations of these focus proteins were subsequently determined by enzyme-linked immunosorbent assay (ELISA) in an independent set of CSF and tumor cyst fluid (CF) samples. Specifically, glioblastoma (GBM) CF had significantly lower GAL, HSPA5, and WNT4 levels than CSF from different grades of glioma. In contrast, IL-6 level was significantly higher in GBM CF when compared with CSF and, among different CSF groups, was highest in GBM CSF. Therefore, these candidate protein biomarkers, identified from both the literatures and in silico analysis, may have potentials in clinical diagnosis, prognosis evaluation, treatment response monitoring, and novel therapeutic targets identification for patients with glioma.

Proteomic approaches in biomarker discovery: new perspectives in cancer diagnostics

Despite remarkable progress in proteomic methods, including improved detection limits and sensitivity, these methods have not yet been established in routine clinical practice. The main limitations, which prevent their integration into clinics, are high cost of equipment, the need for highly trained personnel, and last, but not least, the establishment of reliable and accurate protein biomarkers or panels of protein biomarkers for detection of neoplasms. Furthermore, the complexity and heterogeneity of most solid tumours present obstacles in the discovery of specific protein signatures, which could be used for early detection of cancers, for prediction of disease outcome, and for determining the response to specific therapies. However, cancer proteome, as the end-point of pathological processes that underlie cancer development and progression, could represent an important source for the discovery of new biomarkers and molecular targets for tailored therapies.

Potential serum biomarkers for glioblastoma diagnostic assessed by proteomic approaches

BACKGROUND

The rapid progress of proteomics over the past years has allowed the discovery of a large number of potential biomarker candidates to improve early tumor diagnosis and therapeutic response, thus being further integrated into clinical environment. High grade gliomas represent one of the most aggressive and treatment-resistant types of human brain cancer, with approximately 9-12 months median survival rate for patients with grade IV glioma (glioblastoma). Using state-of-the-art proteomics technologies, we have investigated the proteome profile for glioblastoma patients in order to identify a novel protein biomarker panel that could discriminate glioblastoma patients from controls and increase diagnostic accuracy.

RESULTS

In this study, SELDI-ToF MS technology was used to screen potential protein patterns in glioblastoma patients serum; furthermore, LC-MS/MS technology was applied to identify the candidate biomarkers peaks. Through these proteomic approaches, three proteins S100A8, S100A9 and CXCL4 were selected as putative biomarkers and confirmed by ELISA. Next step was to validate the above mentioned molecules as biomarkers through identification of protein expression by Western blot in tumoral versus peritumoral tissue.

CONCLUSIONS

Proteomic technologies have been used to investigate the protein profile of glioblastoma patients and established several potential diagnostic biomarkers. While it is unlikely for a single biomarker to be highly effective for glioblastoma diagnostic, our data proposed an alternative and efficient approach by using a novel combination of multiple biomarkers.

Advances in the proteomic discovery of novel therapeutic targets in cancer

Proteomic approaches are continuing to make headways in cancer research by helping to elucidate complex signaling networks that underlie tumorigenesis and disease progression. This review describes recent advances made in the proteomic discovery of drug targets for therapeutic development. A variety of technical and methodological advances are overviewed with a critical assessment of challenges and potentials. A number of potential drug targets, such as baculoviral inhibitor of apoptosis protein repeat-containing protein 6, macrophage inhibitory cytokine 1, phosphoglycerate mutase 1, prohibitin 1, fascin, and pyruvate kinase isozyme 2 were identified in the proteomic analysis of drug-resistant cancer cells, drug action, and differential disease state tissues. Future directions for proteomics-based target identification and validation to be more translation efficient are also discussed.

Apoptosis-related gene expression in glioblastoma (LN-18) and medulloblastoma (Daoy) cell lines

The expression of apoptosis genes in a commercial pre-designed low-density array from Applied Biosystems was evaluated in two human brain cancer cell models, LN-18 and Daoy (HTB-186™) in comparison to the reference human primary endothelial cells under basic conditions. Analysis of the gene expression in the cancer cell lines compared to the normal control revealed features reflecting anti-apoptotic and inflammatory characteristics of the former. There was an overall downregulation of apoptosis-stimulating genes in both cancer cell lines, along with an upregulation of certain apoptosis inhibitors. A number of genes demonstrated statistically significant changes in their expressions, including BAX (BCL2-associated X protein); the CARD4/NLR family, CARD domain containing 4; CASP10 (caspase 10, apoptosis-related cysteine peptidase); DAP1 (death-associated protein kinase 1), and BIRC5 (baculoviral IAP repeat-containing 5). Anti-apoptotic potential in both cell lines was demonstrated by changes in the Bax:Bcl-2 ratio and downregulation of the APAF1 gene in LN18 cells. There was also significant downregulation of extrinsic signals and the TNF/FADD/inflammatory cascade, and upregulation of caspase inhibitors (IAPs). These results provided a novel molecular characterization of important human cancer cell lines, which might provide a useful research tool for investigating the experimental model of the CNS cell.

Multi-study integration of brain cancer transcriptomes reveals organ-level molecular signatures

We utilized abundant transcriptomic data for the primary classes of brain cancers to study the feasibility of separating all of these diseases simultaneously based on molecular data alone. These signatures were based on a new method reported herein – Identification of Structured Signatures and Classifiers (ISSAC) – that resulted in a brain cancer marker panel of 44 unique genes. Many of these genes have established relevance to the brain cancers examined herein, with others having known roles in cancer biology. Analyses on large-scale data from multiple sources must deal with significant challenges associated with heterogeneity between different published studies, for it was observed that the variation among individual studies often had a larger effect on the transcriptome than did phenotype differences, as is typical. For this reason, we restricted ourselves to studying only cases where we had at least two independent studies performed for each phenotype, and also reprocessed all the raw data from the studies using a unified pre-processing pipeline. We found that learning signatures across multiple datasets greatly enhanced reproducibility and accuracy in predictive performance on truly independent validation sets, even when keeping the size of the training set the same. This was most likely due to the meta-signature encompassing more of the heterogeneity across different sources and conditions, while amplifying signal from the repeated global characteristics of the phenotype. When molecular signatures of brain cancers were constructed from all currently available microarray data, 90% phenotype prediction accuracy, or the accuracy of identifying a particular brain cancer from the background of all phenotypes, was found. Looking forward, we discuss our approach in the context of the eventual development of organ-specific molecular signatures from peripheral fluids such as the blood.

From a multi-study, integrated transcriptomic dataset, we identified a marker panel for differentiating major human brain cancers at the gene-expression level. The ISSAC molecular signatures for brain cancers, composed of 44 unique genes, are based on comparing expression levels of pairs of genes, and phenotype prediction follows a diagnostic hierarchy. We found that sufficient dataset integration across multiple studies greatly enhanced diagnostic performance on truly independent validation sets, whereas signatures learned from only one dataset typically led to high error rate. Molecular signatures of brain cancers, when obtained using all currently available gene-expression data, achieved 90% phenotype prediction accuracy. Thus, our integrative approach holds significant promise for developing organ-level, comprehensive, molecular signatures of disease.

Extracellular vesicles as prospective carriers of oncogenic protein signatures in adult and paediatric brain tumours

Extracellular vesicles (EVs), including exosomes, act as biological effectors and as carriers of oncogenic signatures in human cancer. The molecular composition and accessibility of EVs in biofluids open unprecedented diagnostic opportunities in malignancies where tumour tissue is difficult to sample, especially in primary and metastatic brain tumours. The ongoing genetic discovery of driver mutations defines the ever increasing numbers of distinct molecular subtypes of brain tumours (orphan diseases), a complexity that may soon be translated into alterations in functional proteins and their oncogenic networks. This may likely be extended to real time changes engendered by the disease progression, tumour heterogeneity, inter-individual variations and therapeutic responses. Meeting these challenges through EV analysis is dependent on technological progress in such areas as generation of mutation- and phospho-specific antibodies, antibody array platforms, nanotechnology, microfluidics, NMR spectroscopy, MS and MRM approaches of quantitative proteomics, which should not be underestimated. Still, vesiculation emerges as a unique process that could be harnessed for the benefit of more individualised patient care.

Investigation of serum proteome alterations in human glioblastoma multiforme

Glioblastoma multiforme (GBM) or grade IV astrocytoma is the most common and lethal adult malignant brain tumor. The present study was conducted to investigate the alterations in the serum proteome in GBM patients compared to healthy controls. Comparative proteomic analysis was performed employing classical 2DE and 2D-DIGE combined with MALDI TOF/TOF MS and results were further validated through Western blotting and immunoturbidimetric assay. Comparison of the serum proteome of GBM and healthy subjects revealed 55 differentially expressed and statistically significant (p <0.05) protein spots. Among the identified proteins, haptoglobin, plasminogen precursor, apolipoprotein A-1 and M, and transthyretin are very significant due to their functional consequences in glioma tumor growth and migration, and could further be studied as glioma biomarkers and grade-specific protein signatures. Analysis of the lipoprotein pattern indicated elevated serum levels of cholesterol, triacylglycerol, and low-density lipoproteins in GBM patients. Functional pathway analysis was performed using multiple software including ingenuity pathway analysis (IPA), protein analysis through evolutionary relationships (PANTHER), database for annotation, visualization and integrated discovery (DAVID), and GeneSpring to investigate the biological context of the identified proteins, which revealed the association of candidate proteins in a few essential physiological pathways such as intrinsic prothrombin activation pathway, plasminogen activating cascade, coagulation system, glioma invasiveness signaling, and PI3K signaling in B lymphocytes. A subset of the differentially expressed proteins was applied to build statistical sample class prediction models for discrimination of GBM patients and healthy controls employing partial least squares discriminant analysis (PLS-DA) and other machine learning methods such as support vector machine (SVM), Decision Tree and Naïve Bayes, and excellent discrimination between GBM and control groups was accomplished.

Proteomic analysis of glioma chemoresistance

Malignant glioma is the most common and destructive form of primary brain tumor. Along with surgery and radiation, chemotherapy remains as the major treatment modality. The emergence of drug resistance, however, often leads to a therapeutic failure in the treatment of glioma, precluding long-term survival of the patients. A proteomic approach has recently been adapted for the mechanistic analysis of glioma drug resistance. The proteomic analysis of drug-resistant glioma led to the discovery of novel biomarkers that can be used for the prognosis of glioma as well as for monitoring the drug response or resistance of glioma. These proteomics-based biomarkers can also be a druggable target that one can exploit for successful glioma chemotherapy. In this review, recent reports on proteomic analysis of glioma from the perspective of chemoresistance are discussed with a focus on the proteome profiles of glioma cells that are resistant to the alkylating agent, 1, 3-bis (2-chloroethyl)-1-nitrosourea (BCNU), as a prime example. Among numerous proteins that were up- or down-regulated in drug-resistant glioma cells, lipocalin 2 (LCN2) and integrin β3 (ITGB3) were identified as key proteins that determine the survival and death of glioma cells. LCN2, ITGB3, and other proteins identified by proteomic analysis could be utilized to overcome glioma chemoresistance.

Identification of serum proteomic biomarkers for early porcine reproductive and respiratory syndrome (PRRS) infection

BACKGROUND

Porcine reproductive and respiratory syndrome (PRRS) is one of the most significant swine diseases worldwide. Despite its relevance, serum biomarkers associated with early-onset viral infection, when clinical signs are not detectable and the disease is characterized by a weak anti-viral response and persistent infection, have not yet been identified. Surface-enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF MS) is a reproducible, accurate, and simple method for the identification of biomarker proteins related to disease in serum. This work describes the SELDI-TOF MS analyses of sera of 60 PRRSV-positive and 60 PRRSV-negative, as measured by PCR, asymptomatic Large White piglets at weaning. Sera with comparable and low content of hemoglobin (< 4.52 μg/mL) were fractionated in 6 different fractions by anion-exchange chromatography and protein profiles in the mass range 1-200 kDa were obtained with the CM10, IMAC30, and H50 surfaces.

RESULTS

A total of 200 significant peaks (p < 0.05) were identified in the initial discovery phase of the study and 47 of them were confirmed in the validation phase. The majority of peaks (42) were up-regulated in PRRSV-positive piglets, while 5 were down-regulated. A panel of 14 discriminatory peaks identified in fraction 1 (pH = 9), on the surface CM10, and acquired at low focus mass provided a serum protein profile diagnostic pattern that enabled to discriminate between PRRSV-positive and -negative piglets with a sensitivity and specificity of 77% and 73%, respectively.

CONCLUSIONS

SELDI-TOF MS profiling of sera from PRRSV-positive and PRRSV-negative asymptomatic piglets provided a proteomic signature with large scale diagnostic potential for early identification of PRRSV infection in weaning piglets. Furthermore, SELDI-TOF protein markers represent a refined phenotype of PRRSV infection that might be useful for whole genome association studies.

Progress in mining the human proteome for disease applications

Currently available technologies allow in-depth analysis of multiple facets of the proteome that have clinical relevance and that complement current genomics-based approaches. Although some progress has been made in our knowledge of the human proteome in health and in disease, there is an urgent need to chart a coherent road map with clearly defined milestones to guide proteomics efforts. Areas of emphasis include: (1) building resources, (2) filling gaps in our understanding of biological variation, and (3) systematically characterizing proteome alterations that occur in well-defined disease states, all of which require an organized and collaborative effort.

A specific miRNA signature in the peripheral blood of glioblastoma patients

The prognosis of patients afflicted by glioblastoma remains poor. Biomarkers for the disease would be desirable in order to allow for an early detection of tumor progression or to indicate rapidly growing tumor subtypes requiring more intensive therapy. In this study, we investigated whether a blood-derived specific miRNA fingerprint can be defined in patients with glioblastoma. To this end, miRNA profiles from the blood of 20 patients with glioblastoma and 20 age- and sex-matched healthy controls were compared. Of 1158 tested miRNAs, 52 were significantly deregulated, as assessed by unadjusted Student's t-test at an alpha level of 0.05. Of these, two candidates, miR-128 (up-regulated) and miR-342-3p (down-regulated), remained significant after correcting for multiple testing by Benjamini-Hochberg adjustment with a p-value of 0.025. The altered expression of these two biomarkers was confirmed in a second cohort of glioblastoma patients and healthy controls by real-time PCR and validated for patients who had received neither radio- nor chemotherapy and for patients who had their glioblastomas resected more than 6 months ago. Moreover, using machine learning, a comprehensive miRNA signature was obtained that allowed for the discrimination between blood samples of glioblastoma patients and healthy controls with an accuracy of 81% [95% confidence interval (CI) 78-84%], specificity of 79% (95% CI 75-83%) and sensitivity of 83% (95% CI 71-85%). In summary, our proof-of-concept study demonstrates that blood-derived glioblastoma-associated characteristic miRNA fingerprints may be suitable biomarkers and warrant further exploration.

The application of SELDI-TOF-MS in clinical diagnosis of cancers

Cancer diagnosis is important, and the early diagnosis of cancers could predict a more successful treatment. The proteomic studies emerged to be useful in combined analyses of samples from patients and provide more accurate diagnosis when compared to the single-factor-based diagnosis. In recent years, cancer detection with surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF MS) is flourishing and brought significant progress in this area. This paper summarizes some recent results with this technique for cancer diagnosis.

Proteomic technologies for the identification of disease biomarkers in serum: advances and challenges ahead

Serum is an ideal biological sample that contains an archive of information due to the presence of a variety of proteins released by diseased tissue, and serum proteomics has gained considerable interest for the disease biomarker discovery. Easy accessibility and rapid protein changes in response to disease pathogenesis makes serum an attractive sample for clinical research. Despite these advantages, the analysis of serum proteome is very challenging due to the wide dynamic range of proteins, difficulty in finding low-abundance target analytes due to the presence of high-abundance serum proteins, high levels of salts and other interfering compounds, variations among individuals and paucity of reproducibility. Sample preparation introduces pre-analytical variations and poses major challenges to analyze the serum proteome. The label-free detection techniques such as surface plasmon resonance, microcantilever, few nanotechniques and different resonators are rapidly emerging for the analysis of serum proteome and they have exhibited potential to overcome few limitations of the conventional techniques. In this article, we will discuss the current status of serum proteome analysis for the biomarker discovery and address key technological advancements, with a focus on challenges and amenable solutions.

Proteomic identification of haptoglobin α2 as a glioblastoma serum biomarker: implications in cancer cell migration and tumor growth

Glioblastoma (GBM; grade IV astrocytoma) is the most malignant and common primary brain tumor in adults. Using combination of 2-DE and MALDI-TOF MS, we analyzed 14 GBM and 6 normal control sera and identified haptoglobin α2 chain as an up-regulated serum protein in GBM patients. GBM-specific up-regulation was confirmed by ELISA based quantitation of haptoglobin (Hp) in the serum of 99 GBM patients as against lower grades (49 grade III/AA; 26 grade II/DA) and 26 normal individuals (p = 0.0001). Further validation using RT-qPCR on an independent set (n = 78) of tumor and normal brain (n = 4) samples and immunohistochemcial staining on a subset (n = 42) of above samples showed increasing levels of transcript and protein with tumor grade and were highest in GBM (p = <0.0001 and <0.0001, respectively). Overexpression of Hp either by stable integration of Hp cDNA or exogenous addition of purified Hp to immortalized astrocytes resulted in increased cell migration. RNAi-mediated silencing of Hp in glioma cells decreased cell migration. Further, we demonstrate that both human glioma and mouse melanoma cells overexpressing Hp showed increased tumor growth. Thus, we have identified haptoglobin as a GBM-specific serum marker with a role on glioma tumor growth and migration.

Glioma proteomics: status and perspectives

High grade gliomas are the most common brain tumors in adults and their malignant nature makes them the fourth biggest cause of cancer death. Major efforts in neuro-oncology research are needed to reach similar progress in treatment efficacy as that achieved for other cancers in recent years. In addition to the urgent need to identify novel effective drug targets against malignant gliomas, the search for glioma biomarkers and grade specific protein signatures will provide a much needed contribution to diagnosis, prognosis, treatment decision and assessment of treatment response. Over the past years glioma proteomics has been attempted at different levels, including proteome analysis of patient biopsies and bodily fluids, of glioma cell lines and animal models. Here we provide an extensive review of the outcome of these studies in terms of protein identifications (protein numbers and regulated proteins), with an emphasis on the methods used and the limitations of the studies with regard to biomarker discovery. This is followed by a perspective on novel technologies and on the potential future contribution of proteomics in a broad sense to understanding glioma biology.