Abstract
Lung cancer is the leading cause of cancer deaths worldwide. Clinically, the treatment of non-small cell lung cancer (NSCLC) can be improved by the early detection and risk screening among population. To meet this need, here we describe in detail a shotgun following the targeted proteomics workflow that we previously applied for human plasma analysis, which involves (1) the application of extensive peptide-level fractionation coupled with label-free quantitative proteomics for the discovery of plasma biomarker candidates for lung cancer and (2) the usage of the multiple reaction monitoring (MRM) assays for the follow-up validations in the verification phase. The workflow features simplicity, low cost, high transferability, high robustness, and flexibility with specific instrumental settings.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jemal A, Siegel R, Ward E et al (2009) Cancer statistics, 2009. CA Cancer J Clin 59(4):225–249
O'Byrne KJ, Danson S, Dunlop D et al (2007) Combination therapy with gefitinib and rofecoxib in patients with platinum-pretreated relapsed non small-cell lung cancer. J Clin Oncol 25(22):3266–3273
Huttenhain R, Malmstrom J, Picotti P et al (2009) Perspectives of targeted mass spectrometry for protein biomarker verification. Curr Opin Chem Biol 13(5–6):518–525
Liu YS, Li C, Xing Z et al (2010) Proteomic mining in the dysplastic liver of WHV/c-myc mice—insights and indicators for early hepatocarcinogenesis. FEBS J 277(19):4039–4053
Eng J, McCormack A, Yates J (1994) An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom 5(11):976–989
Perkins DN, Pappin DJ, Creasy DM et al (1999) Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20(18):3551–3567
Cox J, Mann M (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 26(12):1367–1372
Zhou H, Dai J, Sheng QH et al (2007) A fully automated 2-D LC-MS method utilizing online continuous pH and RP gradients for global proteome analysis. Electrophoresis 28(23):4311–4319
Tang LY, Deng N, Wang LS et al (2007) Quantitative phosphoproteome profiling of Wnt3a-mediated signaling network: indicating the involvement of ribonucleoside-diphosphate reductase M2 subunit phosphorylation at residue serine 20 in canonical Wnt signal transduction. Mol Cell Proteomics 6(11):1952–1967
Deng WJ, Nie S, Dai J et al (2010) Proteome, phosphoproteome, and hydroxyproteome of liver mitochondria in diabetic rats at early pathogenic stages. Mol Cell Proteomics 9(1):100–116
Keshishian H, Addona T, Burgess M et al (2007) Quantitative, multiplexed assays for low abundance proteins in plasma by targeted mass spectrometry and stable isotope dilution. Mol Cell Proteomics 6(12):2212–2229
Taylor IW, Linding R, Warde-Farley D et al (2009) Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol 27(2):199–204
Peat JK, Barton B (2005) Medical statistics : a guide to data analysis and critical appraisal, 1st edn. Blackwell Publishers, Malden, MA. xii, 324p
Breiman L, Friedman RA, Olshen RA (1984) In: Group WI (ed) Classification and regression trees. Belmont, Chapman and Hall
Patz EF Jr, Campa MJ, Gottlin EB et al (2007) Panel of serum biomarkers for the diagnosis of lung cancer. J Clin Oncol 25(35):5578–5583
O'Shaughnessy DF, Atterbury C, Bolton Maggs P et al (2004) Guidelines for the use of fresh-frozen plasma, cryoprecipitate and cryosupernatant. Br J Haematol 126(1):11–28
Mitchell BL, Yasui Y, Li CI et al (2005) Impact of freeze-thaw cycles and storage time on plasma samples used in mass spectrometry based biomarker discovery projects. Cancer Inform 1:98–104
Carvalho PC, Lima DB, Leprevost FV et al (2016) Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0. Nat Protoc 11(1):102–117
Liu YS, Luo XY, Li QR et al (2012) Shotgun and targeted proteomics reveal that pre-surgery serum levels of LRG1, SAA, and C4BP may refine prognosis of resected squamous cell lung cancer. J Mol Cell Biol 4(5):344–347
Li RX, Chen HB, Tu K et al (2008) Localized-statistical quantification of human serum proteome associated with type 2 diabetes. PLoS One 3(9):e3224
Kuzyk MA, Smith D, Yang J et al (2009) Multiple reaction monitoring-based, multiplexed, absolute quantitation of 45 proteins in human plasma. Mol Cell Proteomics 8(8):1860–1877
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media LLC
About this protocol
Cite this protocol
Li, QR., Liu, YS., Zeng, R. (2017). Shotgun and Targeted Plasma Proteomics to Predict Prognosis of Non-Small Cell Lung Cancer. In: Greening, D., Simpson, R. (eds) Serum/Plasma Proteomics. Methods in Molecular Biology, vol 1619. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7057-5_26
Download citation
DOI: https://doi.org/10.1007/978-1-4939-7057-5_26
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-7056-8
Online ISBN: 978-1-4939-7057-5
eBook Packages: Springer Protocols