SILAC-based quantitative proteomic analysis of gastric cancer secretome

Institute of Bioinformatics, International Technology Park, Bangalore, 560066, India.
PROTEOMICS - CLINICAL APPLICATIONS (Impact Factor: 2.68). 06/2013; 7(5-6). DOI: 10.1002/prca.201200069
Source: PubMed

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 2,205 proteins in the gastric cancer secretome of which 263 proteins were overexpressed >4-fold 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.

  • [Show abstract] [Hide abstract]
    ABSTRACT: Owing to recent advances in proteomics analytical methods coupled with bioinformatics capabilities there is a growing trend towards using these capabilities for the development of drugs to treat human disease, including target and drug evaluation, understanding mechanisms of action, and biomarker discovery. Currently the genetic sequences of many major organisms are available, which has helped greatly in characterizing proteomes in model animal systems and in humans. Through proteomics, global profiles of different disease states can be characterized (e.g. changes in types and relative levels as well as post-translational modifications such as glycosylation or phosphorylation). Although intracellular proteomics can provide a broad overview of physiology of cells and tissues, it has been difficult to quantify the low abundance proteins which can be important for understanding the diseased states and treatment progression. For this reason, there is increasing interest in coupling comparative proteomics methods with subcellular fractionation and enrichment techniques for membranes, nucleus, phosphoproteome, glycoproteome as well as low abundance serum proteins. In this review, we will provide examples of where the utilization of different proteomics-coupled enrichment techniques has aided target and biomarker discovery, understanding the targeting mechanism, and mAb discovery. Taken together, these improvements will help to provide a better understanding of the pathophysiology of various diseases including cancer, autoimmunity, inflammation, cardiovascular, and neurological conditions, and in the design and development of better medicines for treating these afflictions. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
    PROTEOMICS - CLINICAL APPLICATIONS 02/2015; 9(1-2). DOI:10.1002/prca.201400097 · 2.68 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Glioblastoma multiforme (GBM) is one of the most aggressive and lethal forms of the primary brain tumors. With predominance of tumor heterogeneity and emergence of new subtypes, new approaches are needed to develop tissue-based markers for tumor typing or circulatory markers to serve as blood-based assays. Multi-omics data integration for GBM tissues would offer new insights on the molecular view of GBM pathogenesis useful to identify biomarker panels. On the other hand, mapping differentially expressed tissue proteins for their secretory potential through bioinformatics analysis or analysis of the tumor cell secretome or tumor exosomes would enhance our understanding of the tumor microenvironment and prospects for targeting circulatory biomarkers. In this review, the authors first present potential biomarker candidates for GBM that have been reported and then focus on plausible pipelines for multi-omic data integration to identify additional, high-confidence molecular panels for clinical applications in GBM.
    Expert Review of Proteomics 08/2014; 11(5). DOI:10.1586/14789450.2014.939634 · 3.54 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Obesity is a major public health threat for many industrialised countries. Bariatric surgery is the most effective treatment against obesity, suggesting that gut derived signals are crucial for energy balance regulation. Several descriptive studies have proven the presence of gastric endogenous systems that modulate energy homeostasis; however, these systems and the interactions between them are still not well known. In the present study, we show for the first time the comparative 2-DE gastric secretome analysis under different nutritional status. We have identified 38 differently secreted proteins by comparing stomach secretomes from tissue explant cultures of rats under feeding, fasting and re-feeding conditions. Among the proteins identified, glyceraldehyde-3-phosphate dehydrogenase was found to be more abundant in gastric secretome and plasma after re-feeding, and downregulated in obesity. Additionally, two calponin-1 species were decreased in feeding state, and other were modulated by nutritional and metabolic conditions. These and other secreted proteins identified in this work may be considered as potential gastrokines implicated in food intake regulation.
    Journal of Proteomics 01/2015; DOI:10.1016/j.jprot.2015.01.001 · 3.93 Impact Factor