Expression of Endoplasmic Reticulum Stress Proteins Is a Candidate Marker of Brain Metastasis in both ErbB-2+ and ErbB-2− Primary Breast Tumors
2011; Elsevier BV; Volume: 179; Issue: 2 Linguagem: Inglês
10.1016/j.ajpath.2011.04.037
ISSN1525-2191
AutoresRebeca Sanz‐Pamplona, Ramón Aragüés, Keltouma Driouch, Berta Martín, Baldo Oliva, Miguel Gil‐Gil, Susana Boluda, Pedro L. Fernández, Antonio Pérez‐Martínez, Vı́ctor Moreno, J.J. Acebes, Rosette Lidereau, Fabien Reyal, Marc J. van de Vijver, Àngels Sierra,
Tópico(s)Lung Cancer Research Studies
ResumoThe increasing incidence of breast cancer brain metastasis in patients with otherwise well-controlled systemic cancer is a key challenge in cancer research. It is necessary to understand the properties of brain-tropic tumor cells to identify patients at risk for brain metastasis. Here we attempt to identify functional phenotypes that might enhance brain metastasis. To obtain an accurate classification of brain metastasis proteins, we mapped organ-specific brain metastasis gene expression signatures onto an experimental protein-protein interaction network based on brain metastatic cells. Thirty-seven proteins were differentially expressed between brain metastases and non-brain metastases. Analysis of metastatic tissues, the use of bioinformatic approaches, and the characterization of protein expression in tumors with or without metastasis identified candidate markers. A multivariate analysis based on stepwise logistic regression revealed GRP94, FN14, and inhibin as the best combination to discriminate between brain and non-brain metastases (ROC AUC = 0.85, 95% CI = 0.73 to 0.96 for the combination of the three proteins). These markers substantially improve the discrimination of brain metastasis compared with ErbB-2 alone (AUC = 0.76, 95% CI = 0.60 to 0.93). Furthermore, GRP94 was a better negative marker (LR = 0.16) than ErbB-2 (LR = 0.42). We conclude that, in breast carcinomas, certain proteins associated with the endoplasmic reticulum stress phenotype are candidate markers of brain metastasis. The increasing incidence of breast cancer brain metastasis in patients with otherwise well-controlled systemic cancer is a key challenge in cancer research. It is necessary to understand the properties of brain-tropic tumor cells to identify patients at risk for brain metastasis. Here we attempt to identify functional phenotypes that might enhance brain metastasis. To obtain an accurate classification of brain metastasis proteins, we mapped organ-specific brain metastasis gene expression signatures onto an experimental protein-protein interaction network based on brain metastatic cells. Thirty-seven proteins were differentially expressed between brain metastases and non-brain metastases. Analysis of metastatic tissues, the use of bioinformatic approaches, and the characterization of protein expression in tumors with or without metastasis identified candidate markers. A multivariate analysis based on stepwise logistic regression revealed GRP94, FN14, and inhibin as the best combination to discriminate between brain and non-brain metastases (ROC AUC = 0.85, 95% CI = 0.73 to 0.96 for the combination of the three proteins). These markers substantially improve the discrimination of brain metastasis compared with ErbB-2 alone (AUC = 0.76, 95% CI = 0.60 to 0.93). Furthermore, GRP94 was a better negative marker (LR = 0.16) than ErbB-2 (LR = 0.42). We conclude that, in breast carcinomas, certain proteins associated with the endoplasmic reticulum stress phenotype are candidate markers of brain metastasis. Brain metastases occur in 10% to 15% of breast cancer patients with advanced disease.1Weil R.J. Palmieri D.C. Bronder J.L. Stark A.M. Steeg P.S. Breast cancer metastasis to the central nervous system.Am J Pathol. 2005; 167: 913-920Abstract Full Text Full Text PDF PubMed Scopus (331) Google Scholar, 2Luck A.A. Evans A.J. Green A.R. Rakha E.A. Paish C. Ellis I.O. The influence of basal phenotype on the metastatic pattern of breast cancer.Clin Oncol (R Coll Radiol). 2008; 20: 40-45Abstract Full Text Full Text PDF PubMed Scopus (60) Google Scholar, 3Tosoni A. Ermani M. Brandes A.A. The pathogenesis and treatment of brain metastases: a comprehensive review.Crit Rev Oncol Hematol. 2004; 52: 199-215Abstract Full Text Full Text PDF PubMed Scopus (109) Google Scholar It can be assumed that up to 30% of metastatic breast cancer patients will undergo brain metastasis during the course of their disease.4Tham Y.L. Sexton K. Kramer R. Hilsenbeck S. Elledge R. Primary breast cancer phenotypes associated with propensity for central nervous system metastases.Cancer. 2006; 107: 696-704Crossref PubMed Scopus (195) Google Scholar, 5Stemmler H.J. Heinemann V. Central nervous system metastases in HER-2-overexpressing metastatic breast cancer: a treatment challenge.Oncologist. 2008; 13: 739-750Crossref PubMed Scopus (50) Google Scholar This rate is increasing, which can be linked to greater survival in patients receiving chemotherapy and to the fact that it is difficult to cross the blood-brain barrier with current systemic treatments.6Carey L.A. Ewend M.G. Metzger R. Sawyer L. Dees E.C. Sartor C.I. Moore D.T. Graham M.L. Central nervous system metastases in women after multimodality therapy for high risk breast cancer.Breast Cancer Res Treat. 2004; 88: 273-280Crossref PubMed Scopus (51) Google Scholar, 7Slimane K. Andre F. Delaloge S. Dunant A. Perez A. Grenier J. Massard C. Spielmann M. Risk factors for brain relapse in patients with metastatic breast cancer.Ann Oncol. 2004; 15: 1640-1644Crossref PubMed Scopus (125) Google Scholar, 8Nathoo N. Chahlavi A. Barnett G.H. Toms S.A. Pathobiology of brain metastases.J Clin Pathol. 2005; 58: 237-242Crossref PubMed Scopus (144) Google Scholar The difficulties in managing brain metastasis therapy result in a median survival of 7 months, with brain metastasis being the cause of death or a major contributing factor in 68% of patients.9Kaal E.C. Niël C.G. Vecht C.J. Therapeutic management of brain metastasis.Lancet Neurol. 2005; 4: 289-298Abstract Full Text Full Text PDF PubMed Scopus (139) Google Scholar Thus, there is a need for both prevention and improved treatment of brain metastasis.2Luck A.A. Evans A.J. Green A.R. Rakha E.A. Paish C. Ellis I.O. The influence of basal phenotype on the metastatic pattern of breast cancer.Clin Oncol (R Coll Radiol). 2008; 20: 40-45Abstract Full Text Full Text PDF PubMed Scopus (60) Google Scholar, 3Tosoni A. Ermani M. Brandes A.A. The pathogenesis and treatment of brain metastases: a comprehensive review.Crit Rev Oncol Hematol. 2004; 52: 199-215Abstract Full Text Full Text PDF PubMed Scopus (109) Google Scholar The association of ErbB-2 overexpression with brain metastasis has been attributed to both the inability of a humanized antibody such as trastuzumab to penetrate the blood-brain barrier10Palmieri D. Smith Q.R. Lockman P.R. Bronder J. Gril B. Chambers A.F. Weil R.J. Steeg P.S. Brain metastases of breast cancer.Breast Dis. 2006; 26: 139-147PubMed Google Scholar and the longer life span of patients receiving therapy that improves visceral disease control.11Bendell J.C. Domchek S.M. Burstein H.J. Harris L. Younger J. Kuter I. Bunnell C. Rue M. Gelman R. Winer E. Central nervous system metastases in women who receive trastuzumab-based therapy for metastatic breast carcinoma.Cancer. 2003; 97: 2972-2977Crossref PubMed Scopus (628) Google Scholar A longer life can lead to the onset of late tumor spread to the central nervous system. The predilection of ErbB-2+ tumor cells for the central nervous system has also been reported.12Palmieri D. Bronder J.L. Herring J.M. Yoneda T. Weil R.J. Stark A.M. Kurek R. Vega-Valle E. Feigenbaum L. Halverson D. Vortmeyer A.O. Steinberg S.M. Aldape K. Steeg P.S. Her-2 overexpression increases the metastatic outgrowth of breast cancer cells in the brain.Cancer Res. 2007; 67: 4190-4198Crossref PubMed Scopus (253) Google Scholar Thus, ErbB-2 may affect the development of breast cancer and increase the potential for brain metastasis. The development of metastasis in the central nervous system depends on the interaction of tumor cells with host defenses and the brain microenvironment, which, surrounded by the blood-brain barrier and lacking lymphatic drainage, differs from lung, liver, lymph node, or bone microenvironments.13Palmieri D. Chambers A.F. Felding-Habermann B. Huang S. Steeg P.S. The biology of metastasis to a sanctuary site.Clin Cancer Res. 2007; 13: 1656-1662Crossref PubMed Scopus (120) Google Scholar Moreover, microenvironmental factors at the metastatic foci may affect the response of tumors to chemotherapy and may condition drug resistance.14Gu B. España L. Méndez O. Torregrosa A. Sierra A. Organ-selective chemoresistance in metastasis from human breast cancer cells: inhibition of apoptosis, genetic variability and microenvironment at the metastatic focus.Carcinogenesis. 2004; 25: 2293-2301Crossref PubMed Scopus (27) Google Scholar Unraveling the biological pathways that drive brain metastasis promises insight into how to limit or prevent this deadly aspect of cancer progression. Our aim was to identify proteins involved in the progression of brain metastasis. Recently, a strategy based on mapping expression profiles with protein interactions has been described.15Chuang H.Y. Lee E. Liu Y.T. Lee D. Ideker T. Network-based classification of breast cancer metastasis.Mol Syst Biol. 2007; 3: 140Crossref PubMed Scopus (1224) Google Scholar The authors show that it is possible to extract relevant biological information about deregulated functions and the relationship between them, and to identify molecules that could be helpful as metastatic markers or therapeutic targets. We compared data obtained from an experimental protein-protein interaction network (PPIN),16Martin B. Aragues R. Sanz R. Oliva B. Boluda S. Martinez A. Sierra A. Biological pathways contributing to organ-specific phenotype of brain metastatic cells.J Proteome Res. 2008; 7: 908-920Crossref PubMed Scopus (21) Google Scholar which identifies biological pathways contributing to the organ-specific phenotype of brain metastatic cells, with gene expression profile data17Landemaine T. Jackson A. Bellahcène A. Rucci N. Sin S. Abad B.M. Sierra A. Boudinet A. Guinebretière J.M. Ricevuto E. Noguès C. Briffod M. Bièche I. Cherel P. Garcia T. Castronovo V. Teti A. Lidereau R. Driouch K. A six-gene signature predicting breast cancer lung metastasis.Cancer Res. 2008; 68: 6092-6099Crossref PubMed Scopus (110) Google Scholar obtained from published transcriptomic analysis of 23 human breast cancer metastasis samples excised from various anatomical locations, including the brain. To compare the expression and network data sets, we mapped the expression values of each gene onto its corresponding protein in the network and searched for proteins whose activities are highly discriminative of brain metastasis. Protein expression analysis of tissues from metastatic human brain and primary breast tumors provided candidate markers of brain metastasis in both ErbB-2+ and ErbB-2− breast carcinomas. The Breast Cancer Committee of the Catalan Institute of Oncology and the University Hospital of Bellvitge supplied samples from patients diagnosed between 1988 and 2006. The series of 122 breast cancers included 71 consecutive primary ductal breast carcinomas at initial diagnosis from metastatic patients in treatment at the time of the study, with one or several organs affected (Table 1), and 51 patients with positive lymph nodes at surgery without metastatic progression after a minimum follow-up duration of 5 years. Three patients had brain as the unique metastasis location and 10 patients had dissemination also at bone (n = 7), lung (n = 6), and liver (n = 4). A total of 48 tumors with bone metastasis, 23 with liver metastasis, and 31 with lung metastasis were included.Table 1Distribution and Combinations of the Various Metastases from Breast Cancer Tumors Included in the Tissue Array AnalysisMetastatic involvement of organsBrainBoneLiverLungTotal (no.)In each organ (no.)13482331As a unique organ [no. (%)]3 (23)11 (23)4 (17)3 (10)21Multimetastatic combinations××4××0××1×××2×××1×××0××5××5×××4××2××××2Other multimetastatic combinations⁎One or more metastases in combination with other organs (lymph nodes, skin, pleura, esophagus, and vagina).24Total number of patients with metastasis: 71. One or more metastases in combination with other organs (lymph nodes, skin, pleura, esophagus, and vagina). Open table in a new tab Total number of patients with metastasis: 71. To optimize each immunohistochemical analysis, the corresponding control tissues for the expression of each protein were also used. To validate protein expression, we included in the analysis six brain metastasis samples matched with the corresponding ductal breast carcinoma to validate protein expression. As a validation set, we used a series of 295 breast tumors for which the transcriptomic data were publicly available.18van de Vijver M.J. He Y.D. van'T Veer L.J. Dai H. Hart A.A. Voskuil D.W. Schreiber G.J. Peterse J.L. Roberts C. Marton M.J. Parrish M. Atsma D. Witteveen A. Glas A. Delahaye L. van der Velde T. Bartelink H. Rodenhuis S. Rutgers E.T. Friend S.H. Bernards R. A gene-expression signature as a predictor of survival in breast cancer.N Engl J Med. 2002; 347: 1999-2009Crossref PubMed Scopus (5256) Google Scholar, 19Bos P.D. Zhang X.H. Nadal C. Shu W. Gomis R.R. Nguyen D.X. Minn A.J. van de Vijver M.J. Gerald W.L. Foekens J.A. Massagué J. Genes that mediate breast cancer metastasis to the brain.Nature. 2009; 459: 1005-1009Crossref PubMed Scopus (1290) Google Scholar The strategy for identifying novel cancer candidates has been described elsewhere.20Aragues R. Sander C. Oliva B. Predicting cancer involvement of genes from heterogeneous data.BMC Bioinformatics. 2008; 9: 172Crossref PubMed Scopus (62) Google Scholar The general procedure of the study, the steps of the analysis, and the levels of protein expression measured are shown as a flow chart in Figure 1A. To identify brain metastasis-associated proteins, we used a prior proteomic analysis that compared differential expression of proteins between 435-P and 435-Br1 cells.16Martin B. Aragues R. Sanz R. Oliva B. Boluda S. Martinez A. Sierra A. Biological pathways contributing to organ-specific phenotype of brain metastatic cells.J Proteome Res. 2008; 7: 908-920Crossref PubMed Scopus (21) Google Scholar Briefly, the proteins differentially expressed by two-dimensional gel electrophoresis (Amersham Ettan DIGE system; GE Healthcare, Little Chalfont, UK) in 435-Br1 cells were identified by peptide mass fingerprinting spectra recorded by a Voyager STR MALDI-TOF system (Applied Biosystems, Foster City, CA) in positive reflector mode with delayed extraction. The spectra were analyzed using the m/z software package (ProteoMetrics, New York, NY). Proteins were identified against a nonredundant database (NCBInr) using online MASCOT search tool (http://www.matrixscience.com/search_form_select.html). The protein network was based on 17 proteins known to be differentially expressed between 435-P breast cancer cells and the brain metastatic variant 435-Br1. We used PIANA21Aragues R. Jaeggi D. Oliva B. PIANA: protein interactions and network analysis.Bioinformatics. 2006; 22: 1015-1017Crossref PubMed Scopus (45) Google Scholar to combine data from DIP 2006.01.16, MIPS 2006.01, HPRD 2005.09.13, BIND 2006.01, and the human interactions from two high-throughput experiments. The final PPIN included 628 proteins from 13 known seeds (interacting proteins) identified by MALDI-TOF (Figure 1B). The protein-network approach for identifying markers of brain metastasis was based on results from a previously analyzed microarray hybridization using the GeneChip human genome U133 Plus 2.0 array (Affymetrix, High Wycombe, UK; Santa Clara, CA), which includes more than 47,000 transcripts and variants, according to standard protocols for RNA extraction and probe preparation.17Landemaine T. Jackson A. Bellahcène A. Rucci N. Sin S. Abad B.M. Sierra A. Boudinet A. Guinebretière J.M. Ricevuto E. Noguès C. Briffod M. Bièche I. Cherel P. Garcia T. Castronovo V. Teti A. Lidereau R. Driouch K. A six-gene signature predicting breast cancer lung metastasis.Cancer Res. 2008; 68: 6092-6099Crossref PubMed Scopus (110) Google Scholar Briefly, to process and normalize Affymetrix chips, robust multichip averaging RMA algorithms were used.22Irizarry R.A. Hobbs B. Collin F. Beazer-Barclay Y.D. Antonellis K.J. Scherf U. Speed T.P. Exploration, normalization, and summaries of high density oligonucleotide array probe level data.Biostatistics. 2003; 4: 249-264Crossref PubMed Scopus (8451) Google Scholar All these computations were performed with the Bioconductor package version 2.0.23Gentleman R.C. Carey V.J. Bates D.M. Bolstad B. Dettling M. Dudoit S. Ellis B. Gautier L. Ge Y. Gentry J. Hornik K. Hothorn T. Huber W. Iacus S. Irizarry R. Leisch F. Li C. Maechler M. Rossini A.J. Sawitzki G. Smith C. Smyth G. Tierney L. Yang J.Y. Zhang J. Bioconductor: open software development for computational biology and bioinformatics.Genome Biol. 2004; 5: R80Crossref PubMed Google Scholar Expression profiles were analyzed with BRB Array tools, version 3.3beta3 (Molecular Statistics and Bioinformatics Section, Biometric Research Branch, Division of Cancer Treatment and Diagnosis, NIH-National Cancer Institute, Bethesda, MD). The univariate t-test was used to identify genes differentially expressed in four brain metastases and metastases in organs other than the brain (5 lung, 6 liver, 2 skin, and 6 osteolytic bone metastases) (Figure 1C). Differences were considered significant when P < 0.001. This stringent threshold was used to limit the number of false positives. These data sets, under the identification number GSE11078, are freely available from the Gene Expression Omnibus (GEO) repository at the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov). Gene expression levels obtained from the microarray experiments were mapped onto the network proteins, assuming that a protein might be differentially expressed if the gene encoding for it was found to be differentially expressed at the RNA level. Differential gene expression was found for 556 of the 658 proteins in the initial PPIN. To classify proteins by function, we used FatiGO software, an online tool for detecting significant associations between gene ontology terms (GO) and groups of genes.24Al-Shahrour F. Minguez P. Tarraga J. Medina I. Alloza E. Montaner D. Dopazo J. FatiGO +: a functional profiling tool for genomic data Integration of functional annotation, regulatory motifs and interaction data with microarray experiments.Nucleic Acids Res. 2007; 35: W91-W96Crossref PubMed Scopus (211) Google Scholar Tissue microarrays (TMAs) were prepared from three representative areas of the tumor that were carefully selected from H&E-stained sections of 122 donor blocks (S.B. and S.H.). Core cylinders, 2 mm in diameter, were removed from each tumor with a skin-biopsy punch and were deposited into recipient paraffin blocks using a specific arraying device (Beecher Instruments, Sun Prairie, WI), as described elsewhere.25Fernández P.L. Nayach I. Fernández E. Fresno L. Palacín A. Farré X. Campo E. Cardesa A. Tissue macroarrays (“microchops”) for gene expression analysis.Virchows Arch. 2001; 438: 591-594Crossref PubMed Scopus (15) Google Scholar Sections (3-μm thick) of the resulting microarray block were cut and used for immunohistochemical (IHC) analysis after being transferred to glass slides. Experimental conditions, positive control tissues, and the characteristics and source of the antibodies used are listed in Table 2. Staining optimization, evaluation parameters, and analyses were established by two pathologists (P.L.F. and S.B.) who were blinded to the clinical status.Table 2Antibodies and Corresponding Conditions for IHCAntibodyCloneSupplier⁎Suppliers: Abcam, Cambridge, UK; AbD S, AbD Serotec, MorphoSys UK, Oxford, UK; Acris, Acris Antibodies, Herford, Germany; SCB, Santa Cruz Biotechnology, Santa Cruz, CA; Sigma, Sigma-Aldrich, St. Louis, MO.ProtocolCellular expressionControl tissueGRP 94sc-1794 (C-19)SCB1/2000†Retrieved in Na-citrate buffer.Endoplasmic reticulumBreast carcinomaTRAF2SM7106P (clon 33A1293; 205–222 aa)Acris1/100 O/N†Retrieved in Na-citrate buffer.CytoplasmBreast carcinomaFN14sc-27143 (C-13)SCB1/3000†Retrieved in Na-citrate buffer.MembraneKidney, heartINHAMCA951ST (R1)AbD S1/50†Retrieved in Na-citrate buffer.CytoplasmTestisTOP1ab3825 (401–600 aa)Abcam1/100‡Retrieved in Tris/EDTA.Nuclei, cytoplasmColorectal tumorVAV2sc-20803 (H-200)SCB1/1000†Retrieved in Na-citrate buffer.CytoplasmPancreasGFAPZ0334Dako1/8000†Retrieved in Na-citrate buffer.CytoplasmBrain (astrocytes)TEM 8ab21270Abcam1/2000†Retrieved in Na-citrate buffer.Cytoplasm, membraneBrain tumor endotheliumARFGAPSP1402PAcris1/1000†Retrieved in Na-citrate buffer.CytoplasmTestisEIF3s8ab19359 (N-terminal 1–50 aa)Abcam1/1000 O/N†Retrieved in Na-citrate buffer.CytoplasmKidneyBAT 8G-6919Sigma1/250†Retrieved in Na-citrate buffer.CytoplasmLymph nodeO/N, antibody is incubated overnight. Suppliers: Abcam, Cambridge, UK; AbD S, AbD Serotec, MorphoSys UK, Oxford, UK; Acris, Acris Antibodies, Herford, Germany; SCB, Santa Cruz Biotechnology, Santa Cruz, CA; Sigma, Sigma-Aldrich, St. Louis, MO.† Retrieved in Na-citrate buffer.‡ Retrieved in Tris/EDTA. Open table in a new tab O/N, antibody is incubated overnight. Antigens were retrieved by heating in a pressure cooker for 7 minutes in the appropriate buffer. Primary antibodies were diluted in Dako real antibody diluent buffer (Dako, Glostrup, Denmark; Carpinteria, CA): Tris buffer, pH 7.2, 15 mmol/L NaN3. LSAB+ system-horseradish peroxidase (Dako) was used, including biotinylated anti-rabbit, anti-mouse, and anti-goat immunoglobulins in PBS; streptavidin conjugated to horseradish peroxidase in PBS; and liquid 3–3′ diaminobenzidine in chromogen solution. A polyclonal antibody anti-ErbB2 (A0485; Dako) was used with an ultraView detection kit in an automatic staining system (Ventana Benchmark XT; Roche, Tucson, AZ). To evaluate the correlation of protein expression with brain metastasis, immunostained samples were graded on a three-category scale (negative, weak positive, and strong positive). The marker was catalogued as overexpressed in strong-positive samples. The association of brain metastasis for each marker was tested using a two-sided Fisher's exact test and summarized by calculating the sensitivity among tumors that developed metastasis, and calculating the specificity among tumors without metastasis, for strong-positive values. Positive and negative likelihood ratios (LR) were also calculated as integrated predictive indexes, as was the area under the ROC curve (AUC). Markers were assessed using a multivariate logistic regression model in a forward stepwise procedure to identify the best combination to discriminate brain metastasis. Because ErbB-2 is a known metastasis risk factor, an analysis including ErbB-2 as the baseline was also performed, as well as a stratified analysis of each candidate marker within ErbB-2+ and ErbB-2− tumors. In all of the analyses, associations were considered significant when P < 0.05. No multiple testing correction was done in this analysis, because the search for the best combination of markers started from a very small set of candidates. We mapped human brain metastasis expression profiles with a PPIN to maximize accuracy in the classification of brain metastasis proteins. The signature of brain genes was catalogued as the organ-specific metastasis signature (BOSMS) with a hierarchical clustering that clearly distinguishes among the different metastases.17Landemaine T. Jackson A. Bellahcène A. Rucci N. Sin S. Abad B.M. Sierra A. Boudinet A. Guinebretière J.M. Ricevuto E. Noguès C. Briffod M. Bièche I. Cherel P. Garcia T. Castronovo V. Teti A. Lidereau R. Driouch K. A six-gene signature predicting breast cancer lung metastasis.Cancer Res. 2008; 68: 6092-6099Crossref PubMed Scopus (110) Google Scholar The BOSMS contained 1193 genes (MetaBre) after the one-versus-all (ONA) class comparisons identified genes differentially expressed in the 4 brain metastases versus the 19 metastases to other organs. Integrating genomic and proteomic analyses, we matched the BOSMS with the PPIN,16Martin B. Aragues R. Sanz R. Oliva B. Boluda S. Martinez A. Sierra A. Biological pathways contributing to organ-specific phenotype of brain metastatic cells.J Proteome Res. 2008; 7: 908-920Crossref PubMed Scopus (21) Google Scholar and obtained 37 organ-specific proteins (Table 3): seven underexpressed and 30 overexpressed. The FatiGO classifier based on GO terms grouped proteins as follows: 13 nucleic acid metabolism proteins (48%), 10 translation proteins (37%), seven cell death proteins (26%), and six modification and folding proteins (22%), as well as a miscellany of metabolic, transport and signaling proteins, some of them with multiple functions (Figure 2). The cellular components of the analysis were as follows: 74% intracellular organelles, 51% cytoplasm, 22% ribonucleoprotein complex proteins, and 15% proteins intrinsic to membrane.Table 3Identities of 37 Brain Metastasis-Specific Proteins Matched in the Proteomic and Transcriptomic Analyses of Human Brain MetastasisGene symbolUniProtKB IDProtein nameFunctionP valueNetwork position (linked to)Up-RegulatedRPL13Q3KQT860S ribosomal protein L13 (breast basic conserved protein 1)Protein biosynthesis0.000840S ribosomal protein s12RPS10P4678340S ribosomal protein S10Protein biosynthesis0.0005RPL5P4677760S ribosomal protein L5Protein biosynthesis0.0002EIF5P55010Eukaryotic translation initiation factor 5Protein biosynthesis0.0007EIF3C (prev. EIF3S8)Q99613Eukaryotic translation initiation factor 3, subunit 8Protein biosynthesis0.00002EEF1DP29692Eukaryotic translation elongation factor 1-delta, isoform 2Signal transduction0.0006EEF1DQ96I38⁎Q96I38 is a secondary accession number. The primary (citable) accession number is number is P29692.Eukaryotic translation elongation factor 1-delta, isoform 1Signal transduction0.0006PARF (syn. C9orf86)Q8IWK1†Both Q8IWK1 and Q9BU21 link to Q3YEC7 as the main UniProtKB record for the putative GTP-binding protein Parf.Putative GTP-binding protein Parf [alt.: C9orf86 protein (fragment)]Signal transduction0.0001INHAP05111Inhibin alpha chainSignal transduction<0.000001CLN3Q13286Protein CLN3Protein folding0.0008FAM3AP98173Protein FAM3A precursor (2–19 protein)No function0.0009PARF (syn. C9orf86)Q9BU21†Both Q8IWK1 and Q9BU21 link to Q3YEC7 as the main UniProtKB record for the putative GTP-binding protein Parf.Putative GTP-binding protein Parf (alt.: C9orf86 protein)No function0.0001TUBB2AP05218Tubulin beta-2 chainStructural0.0004Root proteinTBCDQ96E74Tubulin-specific chaperone DStructural0.00005Tubulin beta-2 chainMCM4P33991DNA replication licensing factor MCM4DNA binding0.0004ARFGAP1Q8N6T3ADP-ribosylation factor GTPase-activating protein 1Transport0.0003EHMT2 (syn. BAT8)Q96KQ7Histone-lysine N-methyltransferase EHMT2 (alt.: HLA-B-associated transcript 8)Methylation0.0008RNF25Q96BH1Ring finger protein 25Ubiquitinization0.0002HMG20BQ9P0W2SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily E member 1-relatedDNA binding0.00001VimentinSIRT6Q8N6T7Sirtuin 6Amino acid metabolism0.000004GFAPP14136Glial fibrillary acidic proteinStructural0.0001TOP1Q9UJN0‡Q9UJN0 is a secondary accession number. The primary (citable) accession number is number is P11387.DNA topoisomerase IDNA binding0.00001CRAMP1L (syn. C16orf34, KIAA1426)Q96RY5Protein cramped-like (alt.: uncharacterized protein KIAA1426)DNA binding0.0003Glyoxalase IC9orf84Q5VXU9Uncharacterized protein C9orf84No function0.0009C16orf34Q9H910Hematological and neurological expressed 1-like proteinNo function0.0003MSH6P52701DNA mismatch repair protein MSH6DNA repair0.00002RAD50TCERG1O14776Transcription elongation regulator 1DNA binding0.00004HSP 70HSP90B1 (prev. TRA1; syn. GRP94)P1462594kDa glucose regulated protein (alt.: GRP94)Protein folding0.0009LINKER (laminin receptor 67 kDa and HSP 27)TRAF2Q12933TNF-receptor associated factor 2Signal transduction0.00007PRDX4TNFRSF12A (syn. FN14)Q9NP84TNF-receptor superfamily member 12A (alt.: fibroblast growth factor-inducible immediate-early response protein 14; alt.: FN14)Receptor0.0001TRAF2Down-RegulatedRPS12P2539840S ribosomal protein S12Protein Biosynthesis0.0006Root proteinRPS23P6226640S ribosomal protein S23Protein biosynthesis0.00000140S ribosomal protein s12DNM3Q6P2G1Dynamin 3Protein biosynthesis0.0008SERPINB9P50453Serpin B9Signal transduction0.0007Tubulin beta-2 chainCREB1Q53X93cAMP responsive element binding protein 1, isoform ATranscription0.000005VimentinCREB1P16220cAMP responsive element binding protein 1, isoform BTranscription0.00005AOC3Q16853Vascular adhesion protein-1Cell adhesion0.0004Glyoxalase Ialt., alternative protein name; prev., previous approved gene symbol; syn., gene symbol synonym appearing in the literature. Q96I38 is a secondary accession number. The primary (citable) accession number is number is P29692.† Both Q8IWK1 and Q9BU21 link to Q3YEC7 as the main UniProtKB record for the putative GTP-binding protein Parf.‡ Q9UJN0 is a secondary accession number. The primary (citable) accession number is number is P11387. Open table in a new tab alt., alternative protein name; prev., previous approved gene symbol; syn., gene symbol synonym appearing in the literature. We graphically represented the brain organ-specific metastasis phenotype (Figure 3) in the PPIN-based functional approach from protein interaction databases, providing a novel hypothesis for pathways involved in brain metastasis progression. Indeed, five functions from the PPIN were predominant: i) DNA binding and repair; ii) protein folding and chaperones, which engage one more DNA binding protein (O14776); iii) structural cytoskeleton, which engages four new DNA binding proteins (Q9P0W2, P33991, Q53X93, and Q9UJN0), two new signal transcription factors (P50453 and P16220), one ubiquitinization protein (Q96BH1), one amino acid metabolism prote
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