Dataset Information

Secretome signature of invasive glioblastoma multiforme.

ABSTRACT: The incurability of malignant glioblastomas is mainly attributed to their highly invasive nature coupled with resistance to chemo- and radiation therapy. Because invasiveness is partially dictated by the proteins these tumors secrete we used SILAC to characterize the secretomes of four glioblastoma cell lines (LN18, T98, U118 and U87). Although U87 and U118 cells both secreted high levels of well-known invasion promoting proteins, a Matrigel invasion assay showed U87 cells to be eight times more invasive than U118 cells, suggesting that additional proteins secreted by U87 cells may contribute to the highly invasive phenotype. Indeed, we identified a number of proteins highly or exclusively expressed by U87 cells as compared to the less invasive cell lines. The most striking of these include ADAM9, ADAM10, cathepsin B, cathepsin L1, osteopontin, neuropilin-1, semaphorin-7A, suprabasin, and chitinase-3-like protein 1. U87 cells also expressed significantly low levels of some cell adhesion proteins such as periostin and EMILIN-1. Correlation of secretome profiles with relative levels of invasiveness using Pavlidis template matching further indicated potential roles for these proteins in U87 glioblastoma invasion. Antibody inhibition of CH3L1 reduced U87 cell invasiveness by 30%.

SUBMITTER: Formolo CA

PROVIDER: S-EPMC3136381 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

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Secretome signature of invasive glioblastoma multiforme.

Formolo Catherine A CA Williams Russell R Gordish-Dressman Heather H MacDonald Tobey J TJ Lee Norman H NH Hathout Yetrib Y

Journal of proteome research 20110531 7

The incurability of malignant glioblastomas is mainly attributed to their highly invasive nature coupled with resistance to chemo- and radiation therapy. Because invasiveness is partially dictated by the proteins these tumors secrete we used SILAC to characterize the secretomes of four glioblastoma cell lines (LN18, T98, U118 and U87). Although U87 and U118 cells both secreted high levels of well-known invasion promoting proteins, a Matrigel invasion assay showed U87 cells to be eight times more ...[more]

PMID: 21574646

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Project description:BackgroundGlioblastoma multiforme (GBM) is a highly lethal, stage IV brain tumour with a prevalence of approximately 2 per 10,000 people globally. The cell surface proteins or surfaceome serve as information gateway in many oncogenic signalling pathways and are important in modulating cancer phenotypes. Dysregulation in surfaceome expression and activity have been shown to promote tumorigenesis. The expression of GBM surfaceome is a case in point; OMICS screening in a cell-based system identified that this sub-proteome is largely perturbed in GBM. Additionally, since these cell surface proteins have 'direct' access to drugs, they are appealing targets for cancer therapy. However, a comprehensive GBM surfaceome landscape has not been fully defined yet. Thus, this study aimed to define GBM-associated surfaceome genes and identify key cell-surface genes that could potentially be developed as novel GBM biomarkers for therapeutic purposes.MethodsWe integrated the RNA-Seq data from TCGA GBM (n = 166) and GTEx normal brain cortex (n = 408) databases to identify the significantly dysregulated surfaceome in GBM. This was followed by an integrative analysis that combines transcriptomics, proteomics and protein-protein interaction network data to prioritize the high-confidence GBM surfaceome signature.ResultsOf the 2381 significantly dysregulated genes in GBM, 395 genes were classified as surfaceome. Via the integrative analysis, we identified 6 high-confidence GBM molecular signature, HLA-DRA, CD44, SLC1A5, EGFR, ITGB2, PTPRJ, which were significantly upregulated in GBM. The expression of these genes was validated in an independent transcriptomics database, which confirmed their upregulated expression in GBM. Importantly, high expression of CD44, PTPRJ and HLA-DRA is significantly associated with poor disease-free survival. Last, using the Drugbank database, we identified several clinically-approved drugs targeting the GBM molecular signature suggesting potential drug repurposing.ConclusionsIn summary, we identified and highlighted the key GBM surface-enriched repertoires that could be biologically relevant in supporting GBM pathogenesis. These genes could be further interrogated experimentally in future studies that could lead to efficient diagnostic/prognostic markers or potential treatment options for GBM.

Project description:BackgroundGliomas are the most common intracranial tumors and are classified as I-IV. Among them, glioblastoma multiforme (GBM) is the most common invasive glioma with a poor prognosis. New molecular biomarkers that can predict clinical outcomes in GBM patients must be identified, which will help comprehend their pathogenesis and supply personalized treatment. Our research revealed four powerful survival indicators in GBM by reanalyzing microarray data and genetic sequencing data in public databases. Moreover, it unraveled new potential therapeutic targets which could help improve the survival time and quality of life of GBM patients.Materials and methodsTo identify prognostic signatures in GBMs, we analyzed the gene profiling data of GBM and standard brain samples from the Gene Expression Omnibus, including four datasets and RNA sequencing data from The Cancer Genome Atlas (TCGA) containing 152 glioblastoma tissues. We performed the differential analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, weighted gene co-expression network analysis (WGCNA) and Cox regression analysis.ResultsAfter differential analysis in GSE12657, GSE15824, GSE42656 and GSE50161, overlapping differentially expressed genes were identified. We identified 110 up-regulated DEGs and 75 down-regulated DEGs in the GBM samples. Significantly enriched subclasses of the GO classification of these genes included mitotic sister chromatid separation, mitotic nuclear division and so on. In KEGG pathway analysis, the most abundant terms were ECM-receptor interaction and protein digestion and absorption. WGCNA analysis was performed on these 185 DEGs in 152 glioblastoma samples obtained from TCGA, and gene co-expression networks were constructed. We then performed a multivariate Cox analysis and established a Cox proportional hazards regression model using the top 20 genes significantly correlated with survival time. We identified a four-protein prognostic signature that could divide patients into high-risk and low-risk groups. Increased expression of SLC12A5, CCL2, IGFBP2, and PDPN was associated with increased risk scores. Finally, the K-M curves confirmed that these genes could be used as independent predictors of survival in patients with glioblastoma.ConclusionOur analytical study identified a set of potential biomarkers that could predict survival and may contribute to successful treatment of GBM patients.

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Secretome signature of invasive glioblastoma multiforme.

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Secretome signature of invasive glioblastoma multiforme.

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