Secretome analysis of Glioblastoma cell line--HNGC-2

Plasma-Derived Extracellular Vesicles Reveal Galectin-3 Binding Protein as Potential Biomarker for Early Detection of Glioma

Gliomas are the most common type of the malignant brain tumor, which arise from glial cells. They make up about 40% of all primary brain tumors and around 70% of all primary malignant brain tumors. They can occur anywhere in the central nervous system (CNS) and have a poor prognosis. The average survival of glioma patients is approximately 6-15 months with poor aspects of life. In this edge, identification of proteins secreted by cancer cells is of special interest because it may provide a better understanding of tumor progression and provide early diagnosis of the diseases. Extracellular vesicles (EVs) were isolated from pooled plasma of healthy controls (n=03) and patients with different grades of glioma (Grade I or II or III, n=03 each). Nanoparticle tracking analysis, western blot, and flow cytometry were performed to determine the size, morphology, the concentration of glioma-derived vesicles and EV marker, CD63. Further, iTRAQ-based LC-MS/MS analysis of EV protein was performed to determine the differential protein abundance in extracellular vesicles across different glioma grades. We further verified galectin-3 binding protein (LGALS3BP) by ELISA in individual blood plasma and plasma-derived vesicles from control and glioma patients (n=40 each). Analysis by Max Quant identified 123 proteins from the pooled patient exosomes, out of which 34, 21, and 14 proteins were found to be differentially abundant by more than 1.3-fold in the different grades of glioma grade I, pilocytic astrocytoma; grade II, diffuse astrocytoma; grade III, anaplastic astrocytoma, respectively, in comparison with the control samples. A total of seven proteins-namely, CRP, SAA2, SERPINA3, SAA1, C4A, LV211, and KV112-showed differential abundance in all the three grades. LGALS3BP was seen to be upregulated across the different grades, and ELISA analysis from individual blood plasma and plasma-derived extracellular vesicles confirmed the increased expression of LGALS3BP in glioma patients (p<0.001). The present study provides LGALS3BP as a potential biomarker for early detection of glioma and improve survival outcome of the patient. The present study further provides the information of progression and monitoring the tumor grades (grade 1, grade II, grade III).

Proteomic Analysis on Anti-Proliferative and Apoptosis Effects of Curcumin Analog, 1,5-bis(4-Hydroxy-3-Methyoxyphenyl)-1,4-Pentadiene-3-One-Treated Human Glioblastoma and Neuroblastoma Cells

Curcumin analogs with excellent biological properties have been synthesized to address and overcome the poor pharmacokinetic profiles of curcumin. This study aims to investigate the cytotoxicity, anti-proliferative, and apoptosis-inducing ability of curcumin analog, MS13 on human glioblastoma U-87 MG, and neuroblastoma SH-SY5Y cells, and to examine the global proteome changes in these cells following treatment. Our current findings showed that MS13 induced potent cytotoxicity and anti-proliferative effects on both cells. Increased caspase-3 activity and decreased bcl-2 concentration upon treatment indicate that MS13 induces apoptosis in these cells in a dose- and time-dependent manner. The label-free shotgun proteomic analysis has defined the protein profiles in both glioblastoma and neuroblastoma cells, whereby a total of nine common DEPs, inclusive of glyceraldehyde 3-phosphate dehydrogenase (GAPDH), alpha-enolase (ENO1), heat shock protein HSP 90-alpha (HSP90AA1), Heat shock protein HSP 90-beta (HSP90AB1), Eukaryotic translation initiation factor 5A-1 (EFI5A), heterogenous nuclear ribonucleoprotein K (HNRNPK), tubulin beta chain (TUBB), histone H2AX (H2AFX), and Protein SET were identified. Pathway analysis further elucidated that MS13 may induce its anti-tumor effects in both cells via the common enriched pathways, “Glycolysis” and “Post-translational protein modification.” Conclusively, MS13 demonstrates an anti-cancer effect that may indicate its potential use in the management of brain malignancies.

A Potential Prognostic Gene Signature for Predicting Survival for Glioblastoma Patients

OBJECTIVE

This study aimed to screen prognostic gene signature of glioblastoma (GBM) to construct prognostic model.

METHODS

Based on the GBM information in the Cancer Genome Atlas (TCGA, training set), prognostic genes (Set X) were screened by Cox regression. Then, the optimized prognostic gene signature (Set Y) was further screened by the Cox-Proportional Hazards (Cox-PH). Next, two prognostic models were constructed: model A was based on the Set Y; model B was based on part of the Set X. The samples were divided into low- and high-risk groups according to the median prognosis index (PI). GBM datasets in Gene Expression Ominous (GEO, GSE13041) and Chinese Glioma Genome Atlas (CGGA) were used as the testing datasets to confirm the prognostic models constructed based on TCGA.

RESULTS

We identified that the prognostic 14-gene signature was significantly associated with the overall survival (OS) in the TCGA. In model A, patients in high- and low-risk groups showed the significantly different OS (P = 7.47 × 10-9, area under curve (AUC) 0.995) and the prognostic ability were also confirmed in testing sets (P=0.0098 and 0.037). The model B in training set was significant but failed in testing sets.

CONCLUSION

The prognostic model which was constructed based on the prognostic 14-gene signature presented a high predictive ability for GBM. The 14-gene signature may have clinical implications in the subclassification of GBM.

Cx43-Associated Secretome and Interactome Reveal Synergistic Mechanisms for Glioma Migration and MMP3 Activation

Extracellular matrix (ECM) remodeling, degradation and glioma cell motility are critical aspects of glioblastoma multiforme (GBM). Despite being a rich source of potential biomarkers and targets for therapeutic advance, the dynamic changes occurring within the extracellular environment that are specific to GBM motility have yet to be fully resolved. The gap junction protein connexin43 (Cx43) increases glioma migration and invasion in a variety of in vitro and in vivo models. In this study, the upregulation of Cx43 in C6 glioma cells induced morphological changes and the secretion of proteins associated with cell motility. Demonstrating the selective engagement of ECM remodeling networks, secretome analysis revealed the near-binary increase of osteopontin and matrix metalloproteinase-3 (MMP3), with gelatinase and NFF-3 assays confirming the proteolytic activities. Informatic analysis of interactome and secretome downstream of Cx43 identifies networks of glioma motility that appear to be synergistically engaged. The data presented here implicate ECM remodeling and matrikine signals downstream of Cx43/MMP3/osteopontin and ARK1B10 inhibition as possible avenues to inhibit GBM.

Proteomics in India: the clinical aspect

Proteomics has emerged as a highly promising bioanalytical technique in various aspects of applied biological research. In Indian academia, proteomics research has grown remarkably over the last decade. It is being extensively used for both basic as well as translation research in the areas of infectious and immune disorders, reproductive disorders, cardiovascular diseases, diabetes, eye disorders, human cancers and hematological disorders. Recently, some seminal works on clinical proteomics have been reported from several laboratories across India. This review aims to shed light on the increasing use of proteomics in India in a variety of biological conditions. It also highlights that India has the expertise and infrastructure needed for pursuing proteomics research in the country and to participate in global initiatives. Research in clinical proteomics is gradually picking up pace in India and its future seems very bright.

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.

Identification of plasma biomarker candidates in glioblastoma using an antibody-array-based proteomic approach

Background

Glioblastoma multiforme (GBM) is a brain tumour with a very high patient mortality rate, with a median survival of 47 weeks. This might be improved by the identification of novel diagnostic, prognostic and predictive therapy-response biomarkers, preferentially through the monitoring of the patient blood. The aim of this study was to define the impact of GBM in terms of alterations of the plasma protein levels in these patients.

Materials and methods.

We used a commercially available antibody array that includes 656 antibodies to analyse blood plasma samples from 17 healthy volunteers in comparison with 17 blood plasma samples from patients with GBM.

Results

We identified 11 plasma proteins that are statistically most strongly associated with the presence of GBM. These proteins belong to three functional signalling pathways: T-cell signalling and immune responses; cell adhesion and migration; and cell-cycle control and apoptosis. Thus, we can consider this identified set of proteins as potential diagnostic biomarker candidates for GBM. In addition, a set of 16 plasma proteins were significantly associated with the overall survival of these patients with GBM. Guanine nucleotide binding protein alpha (GNAO1) was associated with both GBM presence and survival of patients with GBM.

Conclusions

Antibody array analysis represents a useful tool for the screening of plasma samples for potential cancer biomarker candidates in small-scale exploratory experiments; however, clinical validation of these candidates requires their further evaluation in a larger study on an independent cohort of patients.

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.