SILAC-based quantitative proteomic analysis of gastric cancer secretome

Authors

  • Arivusudar Marimuthu,

    1. Institute of Bioinformatics, International Technology Park, Bangalore, India
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  • Yashwanth Subbannayya,

    1. Institute of Bioinformatics, International Technology Park, Bangalore, India
    2. Rajiv Gandhi University of Health Sciences, Bangalore, India
    3. Department of Biochemistry, Kidwai Memorial Institute of Oncology, Bangalore, India
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  • Nandini A. Sahasrabuddhe,

    1. Institute of Bioinformatics, International Technology Park, Bangalore, India
    2. Manipal University, Manipal, India
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  • Lavanya Balakrishnan,

    1. Institute of Bioinformatics, International Technology Park, Bangalore, India
    2. Department of Biotechnology, Kuvempu University, Shankaraghatta, India
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  • Nazia Syed,

    1. Institute of Bioinformatics, International Technology Park, Bangalore, India
    2. Department of Biochemistry and Molecular Biology, Pondicherry University, Puducherry, India
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  • Nirujogi Raja Sekhar,

    1. Institute of Bioinformatics, International Technology Park, Bangalore, India
    2. Centre of Excellence in Bioinformatics, School of Life Sciences, Pondicherry University, Puducherry, India
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  • Teesta V. Katte,

    1. Institute of Bioinformatics, International Technology Park, Bangalore, India
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  • Sneha M. Pinto,

    1. Institute of Bioinformatics, International Technology Park, Bangalore, India
    2. Manipal University, Manipal, India
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  • Srinivas M. Srikanth,

    1. Institute of Bioinformatics, International Technology Park, Bangalore, India
    2. Centre of Excellence in Bioinformatics, School of Life Sciences, Pondicherry University, Puducherry, India
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  • Praveen Kumar,

    1. Institute of Bioinformatics, International Technology Park, Bangalore, India
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  • Harsh Pawar,

    1. Institute of Bioinformatics, International Technology Park, Bangalore, India
    2. Rajiv Gandhi University of Health Sciences, Bangalore, India
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  • Manoj K. Kashyap,

    1. Institute of Bioinformatics, International Technology Park, Bangalore, India
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  • Jagadeesha Maharudraiah,

    1. Institute of Bioinformatics, International Technology Park, Bangalore, India
    2. Manipal University, Manipal, India
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  • Hassan Ashktorab,

    1. Department of Medicine, Howard University, WA, USA
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  • Duane T. Smoot,

    1. Department of Medicine, Meharry Medical College, Nashville, TN, USA
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  • Girija Ramaswamy,

    1. Rajiv Gandhi University of Health Sciences, Bangalore, India
    2. Department of Biochemistry, Kidwai Memorial Institute of Oncology, Bangalore, India
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  • Rekha V. Kumar,

    1. Department of Pathology, Kidwai Memorial Institute of Oncology, Bangalore, India
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  • Yulan Cheng,

    1. Department of Medicine, Division of Gastroenterology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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  • Stephen J. Meltzer,

    1. Department of Medicine, Division of Gastroenterology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
    2. Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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  • Juan Carlos Roa,

    1. Department of Pathology, Universidad de La Frontera, Temuco, Chile
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  • Raghothama Chaerkady,

    1. Institute of Bioinformatics, International Technology Park, Bangalore, India
    2. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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  • T. S. Keshava Prasad,

    1. Institute of Bioinformatics, International Technology Park, Bangalore, India
    2. Manipal University, Manipal, India
    3. Centre of Excellence in Bioinformatics, School of Life Sciences, Pondicherry University, Puducherry, India
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  • H. C. Harsha,

    1. Institute of Bioinformatics, International Technology Park, Bangalore, India
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  • Aditi Chatterjee,

    1. Institute of Bioinformatics, International Technology Park, Bangalore, India
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  • Akhilesh Pandey

    Corresponding author
    1. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
    2. Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, MD, USA
    3. Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
    • Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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  • See Addendum for full list of Affiliations.

  • Colour Online: See the article online to view Figs. 1–5 in colour.

Correspondence: Dr. Akhilesh Pandey, McKusick-Nathans Institute of Genetic Medicine, 733 N. Broadway, BRB 527, Johns Hopkins University, Baltimore, MD 21205, USA

E-mail: pandey@jhmi.edu

Fax: +1-410–502-7544

Abstract

Purpose

Gastric cancer is a commonly occurring cancer in Asia and one of the leading causes of cancer deaths. However, there is no reliable blood-based screening test for this cancer. Identifying proteins secreted from tumor cells could lead to the discovery of clinically useful biomarkers for early detection of gastric cancer.

Experimental design

A SILAC-based quantitative proteomic approach was employed to identify secreted proteins that were differentially expressed between neoplastic and non-neoplastic gastric epithelial cells. Proteins from the secretome were subjected to SDS-PAGE and SCX-based fractionation, followed by mass spectrometric analysis on an LTQ-Orbitrap Velos mass spectrometer. Immunohistochemical labeling was employed to validate a subset of candidates using tissue microarrays.

Results

We identified 2205 proteins in the gastric cancer secretome of which 263 proteins were overexpressed greater than fourfold in gastric cancer-derived cell lines as compared to non-neoplastic gastric epithelial cells. Three candidate proteins, proprotein convertase subtilisin/kexin type 9 (PCSK9), lectin mannose binding 2 (LMAN2), and PDGFA-associated protein 1 (PDAP1) were validated by immunohistochemical labeling.

Conclusions and clinical relevance

We report here the largest cancer secretome described to date. The novel biomarkers identified in the current study are excellent candidates for further testing as early detection biomarkers for gastric adenocarcinoma.

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