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

Circulating Biomarkers for Glioma: A Review

Accurate circulating biomarkers have potential clinical applications in population screening, tumor subclassification, monitoring tumor status, and the delivery of individualized treatments resulting from tumor genotyping. Recently, significant progress has been made within this field in several cancer types, but despite the many potential benefits, currently there is no validated circulating biomarker test for patients with glioma. A number of circulating factors have been examined, including circulating tumor cells, cell-free DNA, microRNA, exosomes, and proteins from both peripheral blood and cerebrospinal fluid with variable results. In the following article, we provide a narrative review of the current evidence pertaining to circulating biomarkers in patients with glioma, including discussion of the advantages and challenges encountered with the current methods used for discovery. Additionally, the potential clinical applications are described with reference to the literature.

Benchmark of site- and structure-specific quantitative tissue N-glycoproteomics for discovery of potential N-glycoprotein markers: a case study of pancreatic cancer

Pancreatic cancer is a highly malignant tumor of the digestive tract that is difficult to diagnose and treat. It is more common in developed countries and has become one of the main causes of death in some countries and regions. Currently, pancreatic cancer generally has a poor prognosis, partly due to the lack of symptoms in the early stages of pancreatic cancer. Therefore, most cases are diagnosed at advanced stage. With the continuous in-depth research of glycoproteomics in precision medical diagnosis, there have been some reports on quantitative analysis of cancer-related cells, plasma or tissues to find specific biomarkers for targeted therapy. This research is based on the developed complete N-linked glycopeptide database search engine GPSeeker, combined with liquid-mass spectrometry and stable diethyl isotope labeling, providing a benchmark of site- and structure-specific quantitative tissue N-glycoproteomics for discovery of potential N-glycoprotein markers. With spectrum-level FDR ≤1%, 20,038 intact N-Glycopeptides corresponding to 4518 peptide backbones, 228 N-glycan monosaccharide compositions 1026 N-glycan putative structures, 4460 N-glycosites and 3437 intact N-glycoproteins were identified. With the criteria of ≥1.5-fold change and p value<0.05, 52 differentially expressed intact N-glycopeptides (DEGPs) were found in pancreatic cancer tussues relative to control, where 38 up-regulated and 14 down-regulated, respectively.

Raman Spectral Signatures of Serum-Derived Extracellular Vesicle-Enriched Isolates May Support the Diagnosis of CNS Tumors

Investigating the molecular composition of small extracellular vesicles (sEVs) for tumor diagnostic purposes is becoming increasingly popular, especially for diseases for which diagnosis is challenging, such as central nervous system (CNS) malignancies. Thorough examination of the molecular content of sEVs by Raman spectroscopy is a promising but hitherto barely explored approach for these tumor types. We attempt to reveal the potential role of serum-derived sEVs in diagnosing CNS tumors through Raman spectroscopic analyses using a relevant number of clinical samples. A total of 138 serum samples were obtained from four patient groups (glioblastoma multiforme, non-small-cell lung cancer brain metastasis, meningioma and lumbar disc herniation as control). After isolation, characterization and Raman spectroscopic assessment of sEVs, the Principal Component Analysis-Support Vector Machine (PCA-SVM) algorithm was performed on the Raman spectra for pairwise classifications. Classification accuracy (CA), sensitivity, specificity and the Area Under the Curve (AUC) value derived from Receiver Operating Characteristic (ROC) analyses were used to evaluate the performance of classification. The groups compared were distinguishable with 82.9-92.5% CA, 80-95% sensitivity and 80-90% specificity. AUC scores in the range of 0.82-0.9 suggest excellent and outstanding classification performance. Our results support that Raman spectroscopic analysis of sEV-enriched isolates from serum is a promising method that could be further developed in order to be applicable in the diagnosis of CNS tumors.

The Multipotential of Leucine-Rich α-2 Glycoprotein 1 as a Clinicopathological Biomarker of Glioblastoma

Leucine-rich α-2 glycoprotein 1 (LRG1) is a diagnostic marker candidate for glioblastoma. Although LRG1 has been associated with angiogenesis, it has been suggested that its biomarker role differs depending on the type of tumor. In this study, a clinicopathological examination of LRG1's role as a biomarker for glioblastoma was performed. We used tumor tissues of 155 cases with diffuse gliomas (27 astrocytomas, 14 oligodendrogliomas, 114 glioblastomas). The immunohistochemical LRG1 intensity scoring was classified into 2 groups: low expression and high expression. Mutations of IDH1, IDH2, and TERT promoter were analyzed through the Sanger method. We examined the relationship between LRG1 expression level in glioblastoma and clinical parameters, such as age, preoperative Karnofsky performance status, tumor location, extent of resection, O6-methylguanine DNA methyltransferase promoter, and prognosis. LRG1 high expression rate was 41.2% in glioblastoma, 3.7% in astrocytoma, and 21.4% in oligodendroglioma. Glioblastoma showed a significantly higher LRG1 expression than lower-grade glioma (p = 0.0003). High expression of LRG1 was an independent favorable prognostic factor (p = 0.019) in IDH-wildtype glioblastoma and correlated with gross total resection (p = 0.002) and the tumor location on nonsubventricular zone (p = 0.00007). LRG1 demonstrated multiple potential as a diagnostic, prognostic, and regional biomarker for glioblastoma.

Putting Proteomics Into Immunotherapy for Glioblastoma

In glioblastoma, the most aggressive brain cancer, a complex microenvironment of heterogeneity and immunosuppression, are considerable hurdles to classify the subtypes and promote treatment progression. Treatments for glioblastoma are similar to standard therapies for many other cancers and do not effectively prolong the survival of patients, due to the unique location and heterogeneous characteristics of glioblastoma. Immunotherapy has shown a promising effect for many other tumors, but its application for glioma still has some challenges. The recent breakthrough of high-throughput liquid chromatography-mass spectrometry (LC-MS/MS) systems has allowed researchers to update their strategy for identifying and quantifying thousands of proteins in a much shorter time with lesser effort. The protein maps can contribute to generating a complete map of regulatory systems to elucidate tumor mechanisms. In particular, newly developed unicellular proteomics could be used to determine the microenvironment and heterogeneity. In addition, a large scale of differentiated proteins provides more ways to precisely classify tumor subtypes and construct a larger library for biomarkers and biotargets, especially for immunotherapy. A series of advanced proteomic studies have been devoted to the different aspects of immunotherapy for glioma, including monoclonal antibodies, oncolytic viruses, dendritic cell (DC) vaccines, and chimeric antigen receptor (CAR) T cells. Thus, the application of proteomics in immunotherapy may accelerate research on the treatment of glioblastoma. In this review, we evaluate the frontline applications of proteomics strategies for immunotherapy in glioblastoma research.

Diagnosis of Glioma Molecular Markers by Terahertz Technologies

This review considers glioma molecular markers in brain tissues and body fluids, shows the pathways of their formation, and describes traditional methods of analysis. The most important optical properties of glioma markers in the terahertz (THz) frequency range are also presented. New metamaterial-based technologies for molecular marker detection at THz frequencies are discussed. A variety of machine learning methods, which allow the marker detection sensitivity and differentiation of healthy and tumor tissues to be improved with the aid of THz tools, are considered. The actual results on the application of THz techniques in the intraoperative diagnosis of brain gliomas are shown. THz technologies’ potential in molecular marker detection and defining the boundaries of the glioma’s tissue is discussed.

Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression

Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disorders and GBM cells release glutamate and show an abnormality, but differ in cellular behavior. So, their etiology is not well understood, nor is it clear how CNS disorders influence GBM behavior or growth. This led us to employ a quantitative analytical framework to unravel shared differentially expressed genes (DEGs) and cell signaling pathways that could link CNS disorders and GBM using datasets acquired from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) datasets where normal tissue and disease-affected tissue were examined. After identifying DEGs, we identified disease-gene association networks and signaling pathways and performed gene ontology (GO) analyses as well as hub protein identifications to predict the roles of these DEGs. We expanded our study to determine the significant genes that may play a role in GBM progression and the survival of the GBM patients by exploiting clinical and genetic factors using the Cox Proportional Hazard Model and the Kaplan-Meier estimator. In this study, 177 DEGs with 129 upregulated and 48 downregulated genes were identified. Our findings indicate new ways that CNS disorders may influence the incidence of GBM progression, growth or establishment and may also function as biomarkers for GBM prognosis and potential targets for therapies. Our comparison with gold standard databases also provides further proof to support the connection of our identified biomarkers in the pathology underlying the GBM progression.

ACOX2 is a prognostic marker and impedes the progression of hepatocellular carcinoma via PPARα pathway

Hepatocellular carcinoma (HCC) has been extensively studied as one of the most aggressive tumors worldwide. However, its mortality rate remains high due to ideal diagnosis and treatment strategies. Uncovering novel genes with prognostic significance would shed light on improving the HCC patient's outcome. In our study, we applied data-independent acquisition (DIA) quantitative proteomics to investigate the expression landscape of 24 paired HCC patients. A total of 1029 differentially expressed proteins (DEPs) were screened. Then, we compared DEPs in our cohort with the differentially expressed genes (DEGs) in The Cancer Genome Atlas, and investigated their prognostic significance, and found 183 prognosis-related genes (PRGs). By conducting protein-protein interaction topological analysis, we identified four subnetworks with prognostic significance. Acyl-CoA oxidase 2 (ACOX2) is a novel gene in subnetwork1, encodes a peroxisomal enzyme, and its function in HCC was investigated in vivo and in vitro. The lower expression of ACOX2 was validated by real-time quantitative PCR, immunohistochemistry, and Western blot. Cell Counting Kit-8 assay, wound healing, and transwell migration assay were applied to evaluate the impact of ACOX2 overexpression on the proliferation and migration abilities in two liver cancer cell lines. ACOX2 overexpression, using a subcutaneous xenograft tumor model, indicated a tumor suppressor role in HCC. To uncover the underlying mechanism, gene set enrichment analysis was conducted, and peroxisome proliferator-activated receptor-α (PPARα) was proposed to be a potential target. In conclusion, we demonstrated a PRG ACOX2, and its overexpression reduced the proliferation and metastasis of liver cancer in vitro and in vivo through PPARα pathway.

What is the role of CRP in glioblastoma?

BACKGROUND

Glioblastoma is the most common primary malignant brain tumor in adults. Previous studies have suggested that CRP (C-reactive protein) could serve as a biomarker candidate as well as a prognostic factor in glioblastoma patients, and we here further investigate its potential role.

MATERIALS AND METHODS

Publicly available datasets were used to compare gene expression between brain samples from glioblastoma patients and non-tumor tissue. The structure of CRP was compared between humans and rats. Glioblastoma cells from humans and rats were stained with anti-CRP. Fischer 344 rats were inoculated with syngeneic glioblastoma cells pre-coated with anti-CRP, and survival was monitored. CRP concentration in rats carrying glioblastoma was followed.

RESULTS

CRP was upregulated on one locus on gene level in glioblastoma tissue as compared to non-tumor brain tissue, but not in glioma stem cells as compared to neural stem cells. The structure of the CRP protein was a characteristic pentamer in both humans and rats. Both human and rat glioblastoma cells were clearly positive for anti-CRP staining. Pre-coating of glioblastoma cells with anti-CRP antibodies did not affect survival in rats with intracranial tumors. Serum levels of CRP increased during tumor progression but did not reach significantly different levels.

CONCLUSIONS

Both human and rat glioblastoma cells could be stained with anti-CRP antibodies in vitro. In a syngeneic glioblastoma rat model we could see an increase in serum CRP during tumor progression, but coating glioblastoma cells with anti-CRP antibodies did not provide any survival change for the animals.

Interaction network of immune-associated genes affecting the prognosis of patients with glioblastoma

Glioblastoma multiforme (GBM) is a common malignant tumor type of the nervous system. The purpose of the present study was to establish a regulatory network of immune-associated genes affecting the prognosis of patients with GBM. The GSE4290, GSE50161 and GSE2223 datasets from the Gene Expression Omnibus database were screened to identify common differentially expressed genes (co-DEGs). A functional enrichment analysis indicated that the co-DEGs were mainly enriched in cell communication, regulation of enzyme activity, immune response, nervous system, cytokine signaling in immune system and the AKT signaling pathway. The co-DEGs accumulated in immune response were then further investigated. For this, the intersection of those co-DEGs and currently known immune-regulatory genes was obtained and a differential expression analysis of these overlapping immune-associated genes was performed. A risk model was established using immune-regulatory genes that affect the prognosis of patients with GBM. The risk score was significantly associated with the prognosis of patients with GBM and had a significant independent predictive value. The risk model had high accuracy in predicting the prognosis of patients with GBM [area under the receiver operating characteristic curve (AUC)=0.764], which was higher than that of a previously reported model of prognosis-associated biomarkers (AUC=0.667). Furthermore, an interaction network was constructed by using immune-regulatory genes and transcription factors affecting the prognosis of patients with GBM and the University of California Santa Cruz database was used to perform a preliminary analysis of the transcription factors and immune genes of interest. The interaction network of immune-regulatory genes constructed in the present study enhances the current understanding of mechanisms associated with poor prognosis of patients with GBM. The risk score model established in the present study may be used to evaluate the prognosis of patients with GBM.

Gene regulation network analysis reveals core genes associated with survival in glioblastoma multiforme

Glioblastoma multiforme (GBM) is a very serious mortality of central nervous system cancer. The microarray data from GSE2223, GSE4058, GSE4290, GSE13276, GSE68848 and GSE70231 (389 GBM tumour and 67 normal tissues) and the RNA-seq data from TCGA-GBM dataset (169 GBM and five normal samples) were chosen to find differentially expressed genes (DEGs). RRA (Robust rank aggregation) method was used to integrate seven datasets and calculate 133 DEGs (82 up-regulated and 51 down-regulated genes). Subsequently, through the PPI (protein-protein interaction) network and MCODE/ cytoHubba methods, we finally filtered out ten hub genes, including FOXM1, CDK4, TOP2A, RRM2, MYBL2, MCM2, CDC20, CCNB2, MYC and EZH2, from the whole network. Functional enrichment analyses of DEGs were conducted to show that these hub genes were enriched in various cancer-related functions and pathways significantly. We also selected CCNB2, CDC20 and MYBL2 as core biomarkers, and further validated them in CGGA, HPA and CCLE database, suggesting that these three core hub genes may be involved in the origin of GBM. All these potential biomarkers for GBM might be helpful for illustrating the important role of molecular mechanisms of tumorigenesis in the diagnosis, prognosis and targeted therapy of GBM cancer.

Small Extracellular Vesicles Isolated from Serum May Serve as Signal-Enhancers for the Monitoring of CNS Tumors

Liquid biopsy-based methods to test biomarkers (e.g., serum proteins and extracellular vesicles) may help to monitor brain tumors. In this proteomics-based study, we aimed to identify a characteristic protein fingerprint associated with central nervous system (CNS) tumors. Overall, 96 human serum samples were obtained from four patient groups, namely glioblastoma multiforme (GBM), non-small-cell lung cancer brain metastasis (BM), meningioma (M) and lumbar disc hernia patients (CTRL). After the isolation and characterization of small extracellular vesicles (sEVs) by nanoparticle tracking analysis (NTA) and atomic force microscopy (AFM), liquid chromatography -mass spectrometry (LC-MS) was performed on two different sample types (whole serum and serum sEVs). Statistical analyses (ratio, Cohen's d, receiver operating characteristic; ROC) were carried out to compare patient groups. To recognize differences between the two sample types, pairwise comparisons (Welch's test) and ingenuity pathway analysis (IPA) were performed. According to our knowledge, this is the first study that compares the proteome of whole serum and serum-derived sEVs. From the 311 proteins identified, 10 whole serum proteins and 17 sEV proteins showed the highest intergroup differences. Sixty-five proteins were significantly enriched in sEV samples, while 129 proteins were significantly depleted compared to whole serum. Based on principal component analysis (PCA) analyses, sEVs are more suitable to discriminate between the patient groups. Our results support that sEVs have greater potential to monitor CNS tumors, than whole serum.

Laminin Subunit Alpha-4 and Osteopontin Are Glioblastoma-Selective Secreted Proteins That Are Increased in the Cerebrospinal Fluid of Glioblastoma Patients

Glioblastoma (GBM) is the most common and aggressive brain tumor in adults. The purpose of the present study was to identify GBM cell-selective secreted proteins by analyzing conditioned media (CM) from GBM, breast, and colon cancer cell lines using sequential window acquisition of all theoretical spectra-mass spectrometry (SWATH-MS) and targeted proteomics. We identified 2371 proteins in the CM from GBM and the other cancer cell lines. Among the proteins identified, 15 showed significantly higher expression in the CM from GBM cell lines than in those from other cancer cell lines. These GBM-selective secreted proteins were further quantified in the cerebrospinal fluid (CSF) from patients with GBM. Laminin subunit alpha-4 (LAMA4) and osteopontin (OPN) had increased expression levels in the CSF from GBM patients compared to those from non-brain tumor patients. In addition, the areas under the curves in a receiver operating characteristic analysis of LAMA4 and OPN were greater than 0.9, allowing for discrimination of GBM patients from non-brain tumor patients. The CSF levels of LAMA4 and OPN were also significantly correlated with the GBM tumor volume. These results suggest that LAMA4 and OPN are secreted from GBM cells into the CSF and appear to be candidates as diagnostic markers and therapeutic targets for GBM.

A mouse SWATH-mass spectrometry reference spectral library enables deconvolution of species-specific proteomic alterations in human tumour xenografts

SWATH-mass spectrometry (MS) enables accurate and reproducible proteomic profiling in multiple model organisms including the mouse. Here, we present a comprehensive mouse reference spectral library (MouseRefSWATH) that permits quantification of up to 10,597 proteins (62.2% of the mouse proteome) by SWATH-MS. We exploit MouseRefSWATH to develop an analytical pipeline for species-specific deconvolution of proteomic alterations in human tumour xenografts (XenoSWATH). This method overcomes the challenge of high sequence similarity between mouse and human proteins, facilitating the study of host microenvironment-tumour interactions from 'bulk tumour' measurements. We apply the XenoSWATH pipeline to characterize an intraductal xenograft model of breast ductal carcinoma in situ and uncover complex regulation consistent with stromal reprogramming, where the modulation of cell migration pathways is not restricted to tumour cells but also operates in the mouse stroma upon progression to invasive disease. MouseRefSWATH and XenoSWATH open new opportunities for in-depth and reproducible proteomic assessment to address wide-ranging biological questions involving this important model organism.

Establishment and validation of highly accurate formalin-fixed paraffin-embedded quantitative proteomics by heat-compatible pressure cycling technology using phase-transfer surfactant and SWATH-MS

The purpose of this study was to establish a quantitative proteomic method able to accurately quantify pathological changes in the protein expression levels of not only non-membrane proteins, but also membrane proteins, using formalin-fixed paraffin-embedded (FFPE) samples. Protein extraction from FFPE sections of mouse liver was increased 3.33-fold by pressure cycling technology (PCT) and reached the same level as protein extraction from frozen sections. After PCT-assisted processing of FFPE liver samples followed by SWATH-MS-based comprehensive quantification, the peak areas of 88.4% of peptides agreed with those from matched fresh samples within a 1.5-fold range. For membrane proteins, this percentage was remarkably increased from 49.1 to 93.8% by PCT. Compared to the conventional method using urea buffer, the present method using phase-transfer surfactant (PTS) buffer at 95 °C showed better agreement of peptide peak areas between FFPE and fresh samples. When our method using PCT and PTS buffer at 95 °C was applied to a bile duct ligation (BDL) disease model, the BDL/control expression ratios for 80.0% of peptides agreed within a 1.2-fold range between FFPE and fresh samples. This heat-compatible FFPE-PCT-SWATH proteomics technology using PTS is suitable for quantitative studies of pathological molecular mechanisms and biomarker discovery utilizing widely available FFPE samples.

A Comprehensive Proteomic SWATH-MS Workflow for Profiling Blood Extracellular Vesicles: A New Avenue for Glioma Tumour Surveillance

Improving outcomes for diffuse glioma patients requires methods that can accurately and sensitively monitor tumour activity and treatment response. Extracellular vesicles (EV) are membranous nanoparticles that can traverse the blood-brain-barrier, carrying oncogenic molecules into the circulation. Measuring clinically relevant glioma biomarkers cargoed in circulating EVs could revolutionise how glioma patients are managed. Despite their suitability for biomarker discovery, the co-isolation of highly abundant complex blood proteins has hindered comprehensive proteomic studies of circulating-EVs. Plasma-EVs isolated from pre-operative glioma grade II-IV patients (n = 41) and controls (n = 11) were sequenced by Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) and data extraction was performed by aligning against a custom 8662-protein library. Overall, 4054 proteins were measured in plasma-EVs. Differentially expressed proteins and putative circulating-EV markers were identified (adj. p-value < 0.05), including those reported in previous in-vitro and ex-vivo glioma-EV studies. Principal component analysis showed that plasma-EV protein profiles clustered according to glioma histological-subtype and grade, and plasma-EVs resampled from patients with recurrent tumour progression grouped with more aggressive glioma samples. The extensive plasma-EV proteome profiles achieved here highlight the potential for SWATH-MS to define circulating-EV biomarkers for objective blood-based measurements of glioma activity that could serve as ideal surrogate endpoints to assess tumour progression and allow more dynamic, patient-centred treatment protocols.

Gelsolin inhibits malignant phenotype of glioblastoma and is regulated by miR-654-5p and miR-450b-5p

We have previously shown that gelsolin (GSN) levels are significantly lower in the blood of patients with glioblastoma (GBM) than in healthy controls. Here, we analyzed the function of GSN in GBM and examined its clinical significance. Furthermore, microRNAs involved in GSN expression were also identified. The expression of GSN was determined using western blot analysis and found to be significantly lower in GBM samples than normal ones. Gelsolin was mainly localized in normal astrocytes, shown using immunohistochemistry and immunofluorescence. Higher expression of GSN was correlated with more prolonged progression‐free survival and overall survival. Gelsolin knockdown using siRNA and shRNA markedly accelerated cell proliferation and invasion in GBM in vitro and in vivo. The inactive form of glycogen synthase kinase‐3β was dephosphorylated by GSN knockdown. In GBM tissues, the expression of GSN and microRNA (miR)‐654‐5p and miR‐450b‐5p showed an inverse correlation. The miR‐654‐5p and miR‐450b‐5p inhibitors enhanced GSN expression, resulting in reduced proliferation and invasion. In conclusion, GSN, which inhibits cell proliferation and invasion, is suppressed by miR‐654‐5p and miR‐450b‐5p in GBM, suggesting that these miRNAs can be targets for treating GBM.

Quantitative SWATH-Based Proteomic Profiling for Identification of Mechanism-Driven Diagnostic Biomarkers Conferring in the Progression of Metastatic Prostate Cancer

Prostate cancer (PCa), the most frequently diagnosed malignancy in men is associated with significant mortality and morbidity. Therefore, demand exists for the identification of potential biomarkers for patient stratification according to prognostic risks and the mechanisms involved in cancer development and progression to avoid over/under treatment of patients and prevent relapse. Quantitative proteomic mass spectrometry profiling and gene enrichment analysis of TGF-β induced-EMT in human Prostate androgen-dependent (LNCaP) and androgen-independent (PC-3) adenocarcinoma cell lines was performed to investigate proteomics involved in Prostate carcinogenesis and their effect onto the survival of PCa patients. Amongst 1,795 proteins, which were analyzed, 474 proteins were significantly deregulated. These proteins contributed to apoptosis, gluconeogenesis, transcriptional regulation, RNA splicing, cell cycle, and MAPK cascade and hence indicating the crucial roles of these proteins in PCa initiation and progression. We have identified a panel of six proteins viz., GOT1, HNRNPA2B1, MAPK1, PAK2, UBE2N, and YWHAB, which contribute to cancer development, and the transition of PCa from androgen dependent to independent stages. The prognostic values of identified proteins were evaluated using UALCAN, GEPIA, and HPA datasets. The results demonstrate the utility of SWATH-LC-MS/MS for understanding the proteomics involved in EMT transition of PCa and identification of clinically relevant proteomic biomarkers.

Quantitative Protein Expression in the Human Retinal Pigment Epithelium: Comparison Between Apical and Basolateral Plasma Membranes With Emphasis on Transporters

Purpose

Retinal pigment epithelium (RPE) limits the xenobiotic entry from the systemic blood stream to the eye. RPE surface transporters can be important in ocular drug distribution, but it has been unclear whether they are expressed on the apical, basal, or both cellular surfaces. In this paper, we provide quantitative comparison of apical and basolateral RPE surface proteomes.

Methods

We separated the apical and basolateral membranes of differentiated human fetal RPE (hfRPE) cells by combining apical membrane peeling and sucrose density gradient centrifugation. The membrane fractions were analyzed with quantitative targeted absolute proteomics (QTAP) and sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) to reveal the membrane protein localization on the RPE cell surfaces. We quantitated 15 transporters in unfractionated RPE cells and scaled their expression to tissue level.

Results

Several proteins involved in visual cycle, cell adhesion, and ion and nutrient transport were expressed on the hfRPE plasma membranes. Most drug transporters showed similar abundance on both RPE surfaces, whereas large neutral amino acids transporter 1 (LAT1), p-glycoprotein (P-gp), and monocarboxylate transporter 1 (MCT1) showed modest apical enrichment. Many solute carriers (SLC) that are potential prodrug targets were present on both cellular surfaces, whereas putative sodium-coupled neutral amino acid transporter 7 (SNAT7) and riboflavin transporter (RFT3) were enriched on the basolateral and sodium- and chloride-dependent neutral and basic amino acid transporter (ATB0+) on the apical membrane.

Conclusions

Comprehensive quantitative information of the RPE surface proteomes was reported for the first time. The scientific community can use the data to further increase understanding of the RPE functions. In addition, we provide insights for transporter protein localization in the human RPE and the significance for ocular pharmacokinetics.

Relationship between circulating inflammatory factors and glioma risk and prognosis: A meta-analysis

BACKGROUND

Inflammatory factors have been considered a significant factor contributing to the development and progression of glioma. However, the relationship between circulating inflammatory factors and glioma risk as well as their prognostic values in glioma patients is still inconclusive. Here, we performed a meta-analysis to address this issue.

METHODS

Relevant articles were identified through PubMed, EMBASE, the Cochrane Library, Web of Science, Wanfang database, and China National Knowledge Infrastructure (CNKI) from inception to February 2019. The weighted mean differences (WMDs) or standard mean differences (SMDs) with 95% confidence intervals (CIs) were used to describe the predictive ability of the levels of circulating inflammatory factors on glioma risk. To evaluate the prognostic values of the circulating inflammatory factors in glioma, hazard ratios (HRs) with 95% CIs were used.

RESULTS

Thirty-one studies comprising 2587 patients were included. The overall analysis showed that increased circulating interleukin-6 (IL-6) [SMD 0.81 (95% CI: 0.21-1.40; P = .008)], interleukin-8 (IL-8) [SMD 1.01 (95% CI: 0.17-1.84; P = .018)], interleukin-17 (IL-17) [SMD 1.12 (95% CI: 0.26-1.98; P = .011)], tumor necrosis factor-α (TNF-α) [SMD 1.80 (95% CI: 1.03-2.56; P = .000)], transforming growth factor-β (TGF-β) [SMD 10.55 (95% CI: 5.59-15.51; P = .000)], and C-reactive protein (CRP) [SMD 0.95 (95% CI: 0.75-1.15; P = .000)] levels were significantly associated with glioma risk. On the other hand, our results showed that circulating IL-6 [HR 1.10 (95% CI: 1.05-1.16; P = .000)] and CRP [HR 2.02 (95% CI: 1.52-2.68; P = .000)] levels were highly correlated with a poor overall survival (OS) rate in glioma patients.

CONCLUSION

Our results indicate that increased circulating IL-6, IL-8, IL-17, TNF-α, TGF-β, and CRP levels are significantly associated with increased glioma risk. Moreover, our meta-analysis suggests that circulating IL-6 and CRP may serve as powerful biomarkers for a poor prognosis in glioma patients.

A Serum Proteomic Signature Predicting Survival in Patients with Glioblastoma

Purpose

Glioblastoma (GBM) is the most common form of brain tumor and has a uniformly poor prognosis. Development of prognostic biomarkers in easily accessible serum samples have the potential to improve the outcomes of patients with GBM through personalized therapy planning.

Material/Methods

In this study pre-treatment serum samples from 30 patients newly diagnosed with GBM were evaluated using a 40-protein multiplex ELISA platform. Analysis of potentially relevant gene targets using The Cancer Genome Atlas database was done using the Glioblastoma Bio Discovery Portal (GBM-BioDP). A ten-biomarker subgroup of clinically relevant molecules was selected using a functional grouping analysis of the 40 plex genes with two genes selected from each group on the basis of degree of variance, lack of co-linearity with other biomarkers and clinical interest. A Multivariate Cox proportional hazard approach was used to analyze the relationship between overall survival (OS), gene expression, and resection status as covariates.

Results

Thirty of 40 of the MSD molecules mapped to known genes within TCGA and separated the patient cohort into two main clusters centered predominantly around a grouping of classical and proneural versus the mesenchymal subtype as classified by Verhaak. Using the values for the 30 proteins in a prognostic index (PI) demonstrated that patients in the entire cohort with a PI below the median lived longer than those patients with a PI above the median (HR 1.8, p=0.001) even when stratified by both age and MGMT status. This finding was also consistent within each Verhaak subclass and highly significant (range p=0.0001-0.011). Additionally, a subset of ten proteins including, CRP, SAA, VCAM1, VEGF, MDC, TNFA, IL7, IL8, IL10, IL16 were found to have prognostic value within the TCGA database and a positive correlation with overall survival in GBM patients who had received gross tumor resection followed by conventional radiation therapy and temozolomide treatment concurrent with the addition of valproic acid.

Conclusion

These findings demonstrate that proteomic approaches to the development of prognostic assays for treatment of GBM may hold potential clinical value.

Evaluation of a biomarker for the diagnosis of pancreas cancer using an animal model

Many approaches have been taken to identify new biomarkers of pancreatic ductal carcinoma (PDC). Since animal models can be sampled under controlled conditions, better standardization is possible compared with heterogeneous human studies. Transgenic rats with conditional activation of oncogenic RAS in pancreatic tissue develop PDC that closely resembles the biological and histopathological features of human PDC. Using this model, we evaluated the usefulness of leucine-rich α2-glycoprotein-1 (LRG-1) as a serum marker. In this study, we found that LRG-1 was overexpressed in rat PDC compared with normal pancreas tissue of the control rats. Serum levels of LRG-1 were also significantly higher in rats bearing PDC than in controls. Importantly, chronic pancreatitis in male Wistar Bonn/Kobori rats, which is a widely accepted as a model of chronic pancreatitis, did not cause serum levels of LRG-1 to become elevated. These results strongly support serum LRG-1 as a candidate biomarker for noninvasive diagnosis of PDC. Our models of pancreas cancer provide a useful strategy for evaluation of candidate markers applicable to human cancer.

Current and Future Trends on Diagnosis and Prognosis of Glioblastoma: From Molecular Biology to Proteomics

Glioblastoma multiforme is the most aggressive malignant tumor of the central nervous system. Due to the absence of effective pharmacological and surgical treatments, the identification of early diagnostic and prognostic biomarkers is of key importance to improve the survival rate of patients and to develop new personalized treatments. On these bases, the aim of this review article is to summarize the current knowledge regarding the application of molecular biology and proteomics techniques for the identification of novel biomarkers through the analysis of different biological samples obtained from glioblastoma patients, including DNA, microRNAs, proteins, small molecules, circulating tumor cells, extracellular vesicles, etc. Both benefits and pitfalls of molecular biology and proteomics analyses are discussed, including the different mass spectrometry-based analytical techniques, highlighting how these investigation strategies are powerful tools to study the biology of glioblastoma, as well as to develop advanced methods for the management of this pathology.

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.

Identification of Blood-Brain Barrier-Permeable Proteins Derived from a Peripheral Organ: In Vivo and in Vitro Evidence of Blood-to-Brain Transport of Creatine Kinase

Certain proteins, such as inflammatory cytokines, that are released from injured or diseased organs are transported from the circulating blood through the blood-brain barrier (BBB) into the brain and contribute to the pathogenesis of related central nervous system dysfunctions. However, little is known about the protein transport mechanisms involved in the central nervous system dysfunctions. The aims of the present study were to identify BBB-permeable protein(s) derived from liver and to clarify their transport characteristics at the BBB. After administration of biotin-labeled liver cytosolic protein fraction to mice in vivo, we identified 9 biotin-labeled proteins in the brain. Among them, we focused here on creatine kinase (CK). In vitro uptake studies with human brain microvessel endothelial cells (hCMEC/D3 cells) showed preferential uptake of muscle-type CK (CK-MM) compared with brain-type CK (CK-BB) at the BBB. Integration plot analysis revealed that CK-MM readily penetrated into brain parenchyma from the circulating blood across the BBB. The uptake of CK-MM by hCMEC/D3 cells was decreased at 4 °C and in the presence of clathrin- and caveolin-dependent endocytosis inhibitors. These results indicate that entry of CK into the brain is mediated by a transport system(s) at the BBB.

Cluster of Differentiation 46 Is the Major Receptor in Human Blood-Brain Barrier Endothelial Cells for Uptake of Exosomes Derived from Brain-Metastatic Melanoma Cells (SK-Mel-28)

Brain metastasis is a frequent complication of cancer and may be mediated, at least in part, by the internalization of cancer-cell-derived exosomes into brain capillary endothelial cells. Clarifying the mechanism(s) of this internalization is of interest because it could help us to develop ways to block brain metastasis, as well as affording a potential new route for drug delivery into the brain. Therefore, the purpose of the present study was to address this issue by identifying the receptors involved in the internalization of exosomes derived from a brain-metastatic cancer cell line (SK-Mel-28) into human blood-brain barrier endothelial cells (hCMEC/D3 cells). The combination of sulfo-SBED-based cross-linking and comprehensive proteomics yielded 20 proteins as exosome receptor candidates in hCMEC/D3 cells. The uptake of PKH67-labeled exosomes by hCMEC/D3 cells measured at 37 °C was significantly reduced by 95.6% at 4 °C and by 15.3% in the presence of 1 mM RGD peptide, an integrin ligand. Therefore, we focused on the identified RGD receptors, integrin α5 and integrin αV, and CD46, which is reported to act as an adenovirus receptor, together with integrin αV. A mixture of neutralizing antibodies against integrin α5 and integrin αV significantly decreased the exosome uptake by 11.8%, while application of CD46 siRNA reduced it by 39.0%. Immunohistochemical analysis confirmed the presence of CD46 in human brain capillary endothelial cells. These results suggest that CD46 is a major receptor for the uptake of SK-Mel-28-derived exosomes by human blood-brain barrier endothelial cells (hCMEC/D3 cells).