Dataset Information

Identification of metastasis-associated proteins in a human tumor metastasis model using the mass-mapping technique.

ABSTRACT: For most cancer cell types, the acquisition of metastatic ability leads to clinically incurable disease. The identification of molecules whose expression is specifically correlated with the metastatic spread of cancer would facilitate the design of therapeutic interventions to inhibit this lethal process. In order to facilitate metastasis gene discovery we have previously characterized a pair of monoclonal cell lines from the human breast carcinoma cell line MDA-MB-435 that have different metastatic phenotypes in immune-compromised mice. In this study, serum-free conditioned media was collected from the cultured monoclonal cell lines and a mass mapping technique was applied in order to profile a component of each cell line proteome. We utilized chromatofocusing in the first dimension to obtain a high resolution separation based on protein pI, and nonporous silica reverse-phase high performance liquid chromatography was used for the second dimension. Selected proteins were identified on the basis of electrospray ionization time of flight mass spectrometry (ESI-TOF MS) intact protein mapping and matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) peptide mass fingerprinting. Using this approach we were able to map over 400 proteins and plot them as a 2-D map of pI versus accurate M(r). This was performed over a pI range of 4.0-6.2, and a mass range of 6-80 kDa. ESI-TOF MS data and further analysis using MALDI-TOF MS confirmed and identified 27 differentially expressed proteins. Proteins associated with the metastatic phenotype included osteopontin and extracellular matrix protein 1, whereas the matrix metalloproteinase-1 and annexin 1 proteins were associated with the non-metastatic phenotype. These findings demonstrate that the mass mapping technique is a powerful tool for the detection and identification of proteins in complex biological samples and which are specifically associated with a cellular phenotype.

SUBMITTER: Kreunin P

PROVIDER: S-EPMC1702405 | biostudies-literature | 2004 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of metastasis-associated proteins in a human tumor metastasis model using the mass-mapping technique.

Kreunin Paweena P Urquidi Virginia V Lubman David M DM Goodison Steve S

Proteomics 20040901 9

For most cancer cell types, the acquisition of metastatic ability leads to clinically incurable disease. The identification of molecules whose expression is specifically correlated with the metastatic spread of cancer would facilitate the design of therapeutic interventions to inhibit this lethal process. In order to facilitate metastasis gene discovery we have previously characterized a pair of monoclonal cell lines from the human breast carcinoma cell line MDA-MB-435 that have different metast ...[more]

PMID: 15352249

Similar Datasets

Project description:Synovial sarcoma (SS) is a highly aggressive soft tissue tumor with high risk of local recurrence and metastasis. However, the mechanisms underlying SS metastasis are still largely unclear. The purpose of this study is to screen metastasis-associated biomarkers in SS by integrated bioinformatics analysis. Two mRNA datasets (GSE40018 and GSE40021) were selected to analyze the differentially expressed genes (DEGs). Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), functional and pathway enrichment analyses were performed for DEGs. Then, the protein-protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. The module analysis of the PPI network and hub genes validation were performed using Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the hub genes were performed using WEB-based GEne SeT AnaLysis Toolkit (WebGestalt). The expression levels and survival analysis of hub genes were further assessed through Gene Expression Profiling Interactive Analysis (GEPIA) and the Kaplan-Meier plotter database. In total, 213 overlapping DEGs were identified, of which 109 were upregulated and 104 were downregulated. GO analysis revealed that the DEGs were predominantly involved in mitosis and cell division. KEGG pathways analysis demonstrated that most DEGs were significantly enriched in cell cycle pathway. GSEA revealed that the DEGs were mainly enriched in oocyte meiosis, cell cycle and DNA replication pathways. A key module was identified and 10 hub genes (CENPF, KIF11, KIF23, TTK, MKI67, TOP2A, CDC45, MELK, AURKB, and BUB1) were screened out. The expression and survival analysis disclosed that the 10 hub genes were upregulated in SS patients and could result in significantly reduced survival. Our study identified a series of metastasis-associated biomarkers involved in the progression of SS, and may provide novel therapeutic targets for SS metastasis.

Project description:Hypoxic areas are a common feature of rapidly growing malignant tumors and their metastases and are typically spatially heterogeneous. Hypoxia has a strong impact on tumor cell biology and contributes to tumor progression in multiple ways. To date, only a few molecular key players in tumor hypoxia, such as hypoxia-inducible factor-1 (HIF-1), have been discovered. The distribution of biomolecules is frequently heterogeneous in the tumor volume and may be driven by hypoxia and HIF-1α. Understanding the spatially heterogeneous hypoxic response of tumors is critical. Mass spectrometric imaging (MSI) provides a unique way of imaging biomolecular distributions in tissue sections with high spectral and spatial resolution. In this paper, breast tumor xenografts grown from MDA-MB-231-HRE-tdTomato cells, with a red fluorescent tdTomato protein construct under the control of a hypoxia response element (HRE)-containing promoter driven by HIF-1α, were used to detect the spatial distribution of hypoxic regions. We elucidated the 3D spatial relationship between hypoxic regions and the localization of lipids and proteins by using principal component analysis-linear discriminant analysis (PCA-LDA) on 3D rendered MSI volume data from MDA-MB-231-HRE-tdTomato breast tumor xenografts. In this study, we identified hypoxia-regulated proteins active in several distinct pathways such as glucose metabolism, regulation of actin cytoskeleton, protein folding, translation/ribosome, splicesome, the PI3K-Akt signaling pathway, hemoglobin chaperone, protein processing in endoplasmic reticulum, detoxification of reactive oxygen species, aurora B signaling/apoptotic execution phase, the RAS signaling pathway, the FAS signaling pathway/caspase cascade in apoptosis, and telomere stress induced senescence. In parallel, we also identified colocalization of hypoxic regions and various lipid species such as PC(16:0/18:0), PC(16:0/18:1), PC(16:0/18:2), PC(16:1/18:4), PC(18:0/18:1), and PC(18:1/18:1), among others. Our findings shed light on the biomolecular composition of hypoxic tumor regions, which may be responsible for a given tumor's resistance to radiation or chemotherapy.

Dataset Information

Identification of metastasis-associated proteins in a human tumor metastasis model using the mass-mapping technique.

Publications

Identification of metastasis-associated proteins in a human tumor metastasis model using the mass-mapping technique.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets