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Core and specific network markers of carcinogenesis from multiple cancer samples. (English) Zbl 1307.92100

Summary: Cancer is the leading cause of death worldwide and is generally caused by mutations in multiple proteins or the dysregulation of pathways. Understanding the causes and the underlying carcinogenic mechanisms can help fight this disease. In this study, a systems biology approach was used to construct the protein-protein interaction (PPI) networks of four cancers and the non-cancers by their corresponding microarray data, PPI modeling and database-mining. By comparing PPI networks between cancer and non-cancer samples to find significant proteins with large PPI changes during carcinogenesis process, core and specific network markers were identified by the intersection and difference of significant proteins, respectively, with carcinogenesis relevance values (CRVs) for each cancer. A total of 28 significant proteins were identified as core network markers in the carcinogenesis of four types of cancer, two of which are novel cancer-related proteins (e.g., UBC and PSMA3). Moreover, seven crucial common pathways were found among these cancers based on their core network markers, and some specific pathways were particularly prominent based on the specific network markers of different cancers (e.g., the RIG-I-like receptor pathway in bladder cancer, the proteasome pathway and TCR pathway in liver cancer, and the HR pathway in lung cancer). Additional validation of these network markers using the literature and new tested datasets could strengthen our findings and confirm the proposed method. From these core and specific network markers, we could not only gain an insight into crucial common and specific pathways in the carcinogenesis, but also obtain a high promising PPI target for cancer therapy.

MSC:

92C42 Systems biology, networks
92C50 Medical applications (general)

Software:

GeneNetwork; DAVID
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[1] Ali, M. J., RB1 gene mutations in retinoblastoma and its clinical correlation, Saudi J. Ophthalmol., 24, 119-123, (2010)
[2] Allenspach, E. J., Notch signaling in cancer, Cancer Biol. Ther., 1, 466-476, (2002), (September-October)
[3] Bardag-Gorce, F., Proteasome inhibitor treatment in alcoholic liver disease, World J. Gastroenterol., 17, 2558-2562, (2011), (May 28)
[4] Bi, G.; Jiang, G., The molecular mechanism of HDAC inhibitors in anticancer effects, Cell Mol. Immunol., 3, 285-290, (2006), (August)
[5] Bioseeker, RIG-I-like Receptor Signaling Pathway in Cancer Drug Pipeline Update 2013, April 2013.
[6] Bland, J. M.; Altman, D. G., Multiple significance tests: the bonferroni method, BMJ, 310, 170, (1995), (January 21)
[7] Bolos, V., Notch signaling in development and cancer, Endocr. Rev., 28, 339-363, (2007), (May)
[8] Camacho, J., Mocular Ontol.: Princ. Recent Adv., (2012)
[9] Cancer Genome Atlas Research, N., Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia, N. Engl. J. Med., 368, 2059-2074, (2013), (May 30)
[10] Cancer Genome Atlas Research, N., Integrated genomic characterization of endometrial carcinoma, Nature, 497, 67-73, (2013), (May 2)
[11] Cavallo, F., 2011: the immune hallmarks of cancer, Cancer Immunol. Immunother., 60, 319-326, (2011), (March)
[12] Chatr-Aryamontri, A., The biogrid interaction database: 2013 update, Nucleic Acids Res., 41, D816-D823, (2013), (January)
[13] Chen, C. H., A novel function of YWHAZ/beta-catenin axis in promoting epithelial-mesenchymal transition and lung cancer metastasis, Mol. Cancer Res., 10, 1319-1331, (2012), (October)
[14] Chu, L. H.; Chen, B. S., Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets, BMC Syst. Biol., 2, 56, (2008)
[15] Chuang, C.-H.; Lin, C.-L., On robust state estimation of gene networks, Biomed. Eng. Comput. Biol., 2, 23-36, (2010)
[16] Codegoni, A. M., Molecular characterisation of a panel of human Ovarian carcinoma xenografts, Eur. J. Cancer, 34, 1432-1438, (1998), (August)
[17] Contessa, J. N., Ionizing radiation activates erb-B receptor dependent akt and p70 S6 kinase signaling in carcinoma cells, Oncogene, 21, 4032-4041, (2002), (June 6)
[18] Cowling, V. H.; Cole, M. D., Mechanism of transcriptional activation by the myc oncoproteins, Semin Cancer Biol., 16, 242-252, (2006), (August)
[19] Cronin, S. J.; Penninger, J. M., From T-cell activation signals to signaling control of anti-cancer immunity, Immunol. Rev., 220, 151-168, (2007), (December)
[20] Dai, P., Function of the lck and fyn in T cell development, Yi Chuan, 34, 289-295, (2012), (March)
[21] Danes, C. G., 14-3-3 zeta down-regulates p53 in mammary epithelial cells and confers luminal filling, Cancer Res., 68, 1760-1767, (2008), (March 15)
[22] Derynck, R., TGF-beta signaling in tumor suppression and cancer progression, Nat. Genet., 29, 117-129, (2001), (Oct)
[23] Fedorova, O. A., Proteomic analysis of the 20S proteasome (PSMA3)-interacting proteins reveals a functional link between the proteasome and mrna metabolism, Biochem. Biophys. Res. Commun., 416, 258-265, (2011), (Dec 16)
[24] Gene Ontology, C., The gene ontology: enhancements for 2011, Nucleic Acids Res., 40, D559-D564, (2012), (January)
[25] Glazier, A. M., Finding genes that underlie complex traits, Science, 298, 2345-2349, (2002), (December 20)
[26] Golub, T. R., Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, Science, 286, 531-537, (1999), (Oct 15)
[27] Guo, H., Identification, modeling and simulation of key pathways underlying certain cancers, Yi Chuan, 33, 809-819, (2011), (August)
[28] Han, H., Identification of differentially expressed genes in pancreatic cancer cells using cdna microarray, Cancer Res., 62, 2890-2896, (2002), (May 15)
[29] Hanahan, D.; Weinberg, R. A., The hallmarks of cancer, Cell, 100, 57-70, (2000), (January 7)
[30] Hanahan, D.; Weinberg, R. A., Hallmarks of cancer: the next generation, Cell, 144, 646-674, (2011), (March 4)
[31] Helleday, T., Homologous recombination in cancer development, treatment and development of drug resistance, Carcinogenesis, 31, 955-960, (2010), (Jun)
[32] Hoeller, D., Ubiquitin and ubiquitin-like proteins in cancer pathogenesis, Nat. Rev. Cancer, 6, 776-788, (2006), (Oct)
[33] Hou, J., Gene expression-based classification of non-small cell lung carcinomas and survival prediction, PLoS One, 5, e10312, (2010)
[34] Huang da, W., Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources, Nat. Protoc., 4, 44-57, (2009)
[35] Hwang, J., Ubiquitin-independent proteasomal degradation during oncogenic viral infections, Biochim. Biophys. Acta, 1816, 147-157, (2011), (December)
[36] Iljin, K., TMPRSS2 fusions with oncogenic ETS factors in prostate cancer involve unbalanced genomic rearrangements and are associated with HDAC1 and epigenetic reprogramming, Cancer Res., 66, 10242-10246, (2006), (November 1)
[37] Ivanov, A. A., Targeting protein-protein interactions as an anticancer strategy, Trends Pharmacol. Sci., 34, 393-400, (2013), (July)
[38] Johansson R., System modeling and identification, 1993.
[39] Kanehisa, M., Molecular network analysis of diseases and drugs in KEGG, Methods Mol. Biol., 939, 263-275, (2013)
[40] Kar, G., Human cancer protein-protein interaction network: a structural perspective, PLoS Comput. Biol., 5, e1000601, (2009), (December)
[41] Kawai, T.; Akira, S., Toll-like receptor and RIG-I-like receptor signaling, Ann. N. Y. Acad. Sci., 1143, 1-20, (2008), (November)
[42] Kim, W. J., Predictive value of progression-related gene classifier in primary non-muscle invasive bladder cancer, Mol. Cancer, 9, 3, (2010)
[43] Komander, D., The emerging complexity of protein ubiquitination, Biochem. Soc. Trans., 37, 937-953, (2009), (October)
[44] Landi, M. T., Gene expression signature of cigarette smoking and its role in lung adenocarcinoma development and survival, PLoS One, 3, e1651, (2008)
[45] Lee, T. Y., An agent-based system to discover protein-protein interactions, identify protein complexes and proteins with multiple peptide mass fingerprints, J. Comput. Chem., 27, 1020-1032, (2006), (Jul 15)
[46] Levine, L., Proteasome inhibitors: their effects on arachidonic acid release from cells in culture and arachidonic acid metabolism in rat liver cells, BMC Pharmacol., 4, 15, (2004), (August 5)
[47] Lin, C. C., Essential core of protein-protein interaction network in Escherichia coli, J. Proteome Res., 8, 1925-1931, (2009), (April)
[48] Lin, C. C., Link clustering reveals structural characteristics and biological contexts in signed molecular networks, PLoS One, 8, e67089, (2013)
[49] Lin, C. L., Control design for signal transduction networks, Bioinform. Biol. Insights, 3, 1-14, (2009)
[50] Lin, L. L., Systems biology of meridians, acupoints, and Chinese herbs in disease,, Evid. Based Complement. Altern. Med., 2012, 372670, (2012)
[51] Lin, L. L., Revealing the molecular mechanism of gastric cancer marker annexin A4 in cancer cell proliferation using exon arrays, PLoS One, 7, e44615, (2012)
[52] Lin, S. Y., HLJ1 is a novel caspase-3 substrate and its expression enhances UV-induced apoptosis in non-small cell lung carcinoma, Nucleic Acids Res., 38, 6148-6158, (2010), (October)
[53] Lin, C. L.; Chuang, C. H., Review of control theory and dynamics in systems biology, Int. J. Syst. Synth. Biol., 1, 39-61, (2010)
[54] Liu, K. Q., Identifying dysregulated pathways in cancers from pathway interaction networks, BMC Bioinform., 13, 126, (2012)
[55] Liu, R., Dynamical network biomarkers for identifying critical transitions and their driving networks of biologic processes, Quant. Biol., 1, 105-114, (2013)
[56] Liu, X., Identifying disease genes and module biomarkers by differential interactions, J. Am. Med. Inform. Assoc., 19, 241-248, (2012), (March-April)
[57] Liu, X., Detecting early-warning signals of type 1 diabetes and its leading biomolecular networks by dynamical network biomarkers, BMC Med. Genomics, 6, Suppl 2, S8, (2013)
[58] Livinskaya, V. A., Polyclonal antibodies against human proteasome subunits PSMA3, PSMA5, and PSMB5, Hybridoma (Larchmt), 31, 272-278, (2012), (August)
[59] Lodish, H., Molecular cell biology, (2000), W. H. Freeman and Company New York
[60] Lodish, H., et al., Molecular Cell Biology, 2013.
[61] Markowitz, S. D.; Bertagnolli, M. M., Molecular origins of cancer: molecular basis of colorectal cancer, N. Engl. J. Med., 361, 2449-2460, (2009), (December 17)
[62] Medeiros, F., Tissue handling for genome-wide expression analysis: a review of the issues, evidence, and opportunities, Arch. Pathol. Lab. Med., 131, 1805-1816, (2007), (December)
[63] Merikangas, K. R., Commentary: understanding sources of complexity in chronic diseases—the importance of integration of genetics and epidemiology, Int. J. Epidemiol., 35, 590-592, (2006), (discussion 593-6, Jun)
[64] Muller, P. A.; Vousden, K. H., P53 mutations in cancer,, Nat. Cell Biol., 15, 2-8, (2013), (January)
[65] Neal, C. L.; Yu, D., 14-3-3 zeta as a prognostic marker and therapeutic target for cancer, Expert Opin. Ther. Targets, 14, 1343-1354, (2010), (December)
[66] Nguyen, V. N., Expression of cyclin D1, ki-67 and PCNA in non-small cell lung cancer: prognostic significance and comparison with p53 and bcl-2, Acta Histochem., 102, 323-338, (2000), (August)
[67] Osada, H., Histone modification in the tgfbetarii gene promoter and its significance for responsiveness to HDAC inhibitor in lung cancer cell lines, Mol. Carcinog., 44, 233-241, (2005), (December)
[68] Pacal, M.; Bremner, R., Insights from animal models on the origins and progression of retinoblastoma, Curr. Mol. Med., 6, 759-781, (2006), (November)
[69] Pagano, M., Gauvreau, K., Principles of biostatistics, 2000. · Zbl 0892.62046
[70] Pedroza-Gonzalez, A., Activated tumor-infiltrating CD4+ regulatory T cells restrain antitumor immunity in patients with primary or metastatic liver cancer, Hepatology, 57, 183-194, (2013), (January)
[71] Peltonen, L.; McKusick, V. A., Genomics and medicine. dissecting human disease in the postgenomic era, Science, 291, 1224-1229, (2001), (February 16)
[72] Polakis, P., Wnt signaling in cancer, Cold Spring Harb. Perspect. Biol., 4, (2012), (May)
[73] Rivlin, N., Mutations in the p53 tumor suppressor gene: important milestones at the various steps of tumorigenesis, Genes Cancer, 2, 466-474, (2011), (April)
[74] Roessler, S., A unique metastasis gene signature enables prediction of tumor relapse in early-stage hepatocellular carcinoma patients, Cancer Res., 70, 10202-10212, (2010), (December 15)
[75] Roessler, S., Integrative genomic identification of genes on 8p associated with hepatocellular carcinoma progression and patient survival, Gastroenterology, e12, 142, 957-966, (2012), (April)
[76] Rybaczyk, L. A., An indicator of cancer: downregulation of monoamine oxidase-A in multiple organs and species, BMC Genomics, 9, 134, (2008)
[77] Sanchez-Palencia, A., Gene expression profiling reveals novel biomarkers in nonsmall cell lung cancer, Int. J. Cancer, 129, 355-364, (2011), (July 15)
[78] Satoh, J., Molecular network of microrna targets in alzheimer׳s disease brains, Exp. Neurol., 235, 436-446, (2012), (Jun)
[79] Saviozzi, S., Non-small cell lung cancer exhibits transcript overexpression of genes associated with homologous recombination and DNA replication pathways, Cancer Res., 69, 3390-3396, (2009), (April 15)
[80] Schneikert, J.; Behrens, J., The canonical wnt signalling pathway and its APC partner in colon cancer development, Gut, 56, 417-425, (2007), (March)
[81] Sheffer, M., Association of survival and disease progression with chromosomal instability: a genomic exploration of colorectal cancer, Proc. Natl. Acad. Sci. USA, 106, 7131-7136, (2009), (April 28)
[82] Stratton, M. R., The cancer genome, Nature, 458, 719-724, (2009), (April 9)
[83] Taniue, K., A member of the ETS family, EHF, and the atpase RUVBL1 inhibit p53-mediated apoptosis, EMBO Rep., 12, 682-689, (2011), (Jul)
[84] Thomas, J. G., An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles, Genome Res., 11, 1227-1236, (2001), (July)
[85] Tun, H. W., Pathway signature and cellular differentiation in clear cell renal cell carcinoma, PLoS One, 5, e10696, (2010)
[86] Uramoto, H., Expression of the p53 family in lung cancer, Anticancer Res., 26, 1785-1790, (2006), (May-June)
[87] Vogl, A., Gene expression profile changes between melanoma metastases and their daughter cell lines: implication for vaccination protocols, J. Invest. Dermatol., 124, 401-404, (2005), (Feb)
[88] Wallace, T. A., Tumor immunobiological differences in prostate cancer between african-American and European-American men, Cancer Res, 68, 927-936, (2008), (February 1)
[89] Wang, C. C., Dimethyl sulfoxide promotes the multiple functions of the tumor suppressor HLJ1 through activator protein-1 activation in NSCLC cells, PLoS One, 7, e33772, (2012)
[90] Wang, Y. C.; Chen, B. S., A network-based biomarker approach for molecular investigation and diagnosis of lung cancer, BMC Med. Genomics, 4, 2, (2011)
[91] Watters, A. D., Genetic aberrations of c-myc and CCND1 in the development of invasive bladder cancer, Br. J. Cancer, 87, 654-658, (2002), (September 9)
[92] Welcsh, P. L., Insights into the functions of BRCA1 and BRCA2, Trends Genet., 16, 69-74, (2000), (Feb)
[93] White, A. W., Protein-protein interactions as targets for small-molecule therapeutics in cancer, Expert Rev. Mol. Med., 10, e8, (2008)
[94] Williams, G. H.; Stoeber, K., The cell cycle and cancer, J. Pathol., 226, 352-364, (2012), (January)
[95] Wood, L. M., The ubiquitin-like protein, ISG15, is a novel tumor-associated antigen for cancer immunotherapy, Cancer Immunol. Immunother., 61, 689-700, (2012), (May)
[96] Wu, C. C., Genenetwork: an interactive tool for reconstruction of genetic networks using microarray data, Bioinformatics, 20, 3691-3693, (2004), (December 12)
[97] Wu, D., Cancer bioinformatics: a new approach to systems clinical medicine, BMC Bioinform., 13, 71, (2012)
[98] Wu, W. S., Different functional gene clusters in yeast have different spatial distributions of the transcription factor binding sites, Bioinform. Biol. Insights, 5, 1-11, (2011)
[99] Wu, W. S.; Li, W. H., Systematic identification of yeast cell cycle transcription factors using multiple data sources, BMC Bioinform., 9, 522, (2008)
[100] Yamasaki, L.; Pagano, M., Cell cycle, proteolysis and cancer, Curr. Opin. Cell Biol., 16, 623-628, (2004), (December)
[101] Yang, T. H.; Wu, W. S., Identifying biologically interpretable transcription factor knockout targets by jointly analyzing the transcription factor knockout microarray and the chip-chip data, BMC Syst. Biol., 6, 102, (2012)
[102] 〈http://www.cancer.gov/cancertopics/understandingcancer/cancer/page9〉
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