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Combination Strategy of Quantitative Proteomics Uncovers the Related Proteins of Colorectal Cancer in the Interstitial Fluid of Colonic Tissue from the AOM-DSS Mouse Model

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Tissue Proteomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1788))

Abstract

Quantitative proteome analysis using iTRAQ is an important technique to find disease-related proteins. As an important component of tumor microenvironment, tissue interstitial fluid (TIF) has drawn a great attention for its potential as a source for exploration of the solid tumor biomarkers. On the basis of a mouse model of colorectal cancer (CRC) that was induced by the carcinogenetic reagents, we adopted a quantitative proteome analysis with iTRAQ to discover the CRC-related proteins in the TIFs and with MRM to evaluate the corresponding abundance changes in the individual mouse TIF and serum samples.

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References

  1. Peddareddigari VG, Wang D, Dubois RN (2010) The tumor microenvironment in colorectal carcinogenesis. Cancer Microenviron 3:149–166

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Taketo MM (2012) Roles of stromal microenvironment in colon cancer progression. J Biochem 151:477–481

    Article  CAS  PubMed  Google Scholar 

  3. Wiig H, Tenstad O, Iversen PO, Kalluri R, Bjerkvig R (2010) Interstitial fluid: the overlooked component of the tumor microenvironment? Fibrogenesis Tissue Repair 3:12

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Gromov P, Gromova I, Olsen CJ, Timmermans-Wielenga V, Talman ML, Serizawa RR, Moreira JM (2013) Tumor interstitial fluid – a treasure trove of cancer biomarkers. Biochim Biophys Acta 1834:2259–2270

    Article  CAS  PubMed  Google Scholar 

  5. Gromov P, Gromova I, Bunkenborg J, Cabezon T, Moreira JM, Timmermans-Wielenga V, Roepstorff P, Rank F, Celis JE (2010) Up-regulated proteins in the fluid bathing the tumour cell microenvironment as potential serological markers for early detection of cancer of the breast. Mol Oncol 4:65–89

    Article  CAS  PubMed  Google Scholar 

  6. Okayasu I, Ohkusa T, Kajiura K, Kanno J, Sakamoto S (1996) Promotion of colorectal neoplasia in experimental murine ulcerative colitis. Gut 39:87–92

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Rosenberg DW, Giardina C, Tanaka T (2009) Mouse models for the study of colon carcinogenesis. Carcinogenesis 30:183–196

    Article  CAS  PubMed  Google Scholar 

  8. Lange V, Picotti P, Domon B, Aebersold R (2008) Selected reaction monitoring for quantitative proteomics: a tutorial. Mol Syst Biol 4:222

    Article  PubMed  PubMed Central  Google Scholar 

  9. Neufert C, Becker C, Neurath MF (2007) An inducible mouse model of colon carcinogenesis for the analysis of sporadic and inflammation-driven tumor progression. Nat Protoc 2(8)

    Google Scholar 

  10. Wisniewski JR, Zougman A, Nagaraj N, Mann M (2009) Universal sample preparation method for proteome analysis. Nat Methods 6:359–362

    Article  CAS  PubMed  Google Scholar 

  11. MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B, Kern R, Tabb DL, Liebler DC, MacCoss MJ (2010) Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26:966–968

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Sadeh NM, Hildum DW, Kjenstad D et al (1999) Mascot: an agent-based architecture for coordinated mixed-initiative supply chain planning and scheduling[C]. In: Workshop on agent-based decision support in managing the internet-enabled supply-chain, at agents’ 99

    Google Scholar 

  13. Searle BC (2010) Scaffold: a bioinformatic tool for validating MS/MS based proteomic studies. Proteomics 10(6):1265–1269

    Article  CAS  PubMed  Google Scholar 

  14. Nesvizhskii AI, Keller A, Kolker E, Aebersold R (2003) A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem 75:4646–4658

    Article  CAS  Google Scholar 

  15. Petersen TN, Brunak S, von Heijne G, Nielsen H (2011) SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods 8:785–786

    Article  CAS  Google Scholar 

  16. Bendtsen JD, Jensen LJ, Blom N, Von Heijne G, Brunak S (2004) Feature-based prediction of non-classical and leaderless protein secretion. Protein Eng Des Sel 17:349–356

    Article  CAS  Google Scholar 

  17. Hood BL, Zhou M, Chan KC, Lucas DA, Kim GJ, Issaq HJ, Veenstra TD, Conrads TP (2005) Investigation of the mouse serum proteome. J Proteome Res 4:1561–1568

    Article  CAS  PubMed  Google Scholar 

  18. Lai KK, Kolippakkam D, Beretta L (2008) Comprehensive and quantitative proteome profiling of the mouse liver and plasma. Hepatology 47:1043–1051

    Article  CAS  PubMed  Google Scholar 

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Acknowledgment

This work was supported by the National Key Basic Research Program of China (2011CB910704) and the National Natural Science Foundation of China (81372601).

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Correspondence to Siqi Liu .

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Hou, G., Wang, Y., Lou, X., Liu, S. (2017). Combination Strategy of Quantitative Proteomics Uncovers the Related Proteins of Colorectal Cancer in the Interstitial Fluid of Colonic Tissue from the AOM-DSS Mouse Model. In: Sarwal, M., Sigdel, T. (eds) Tissue Proteomics. Methods in Molecular Biology, vol 1788. Humana Press, New York, NY. https://doi.org/10.1007/7651_2017_88

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  • DOI: https://doi.org/10.1007/7651_2017_88

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7852-6

  • Online ISBN: 978-1-4939-7854-0

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