Literature DB >> 17022644

Quantitative proteomic and genomic profiling reveals metastasis-related protein expression patterns in gastric cancer cells.

Yet-Ran Chen1, Hsueh-Fen Juan, Hsuan-Cheng Huang, Hsin-Hung Huang, Ya-Jung Lee, Mei-Yueh Liao, Chien-Wei Tseng, Li-Ling Lin, Jeou-Yuan Chen, Mei-Jung Wang, Jenn-Han Chen, Yu-Ju Chen.   

Abstract

Gastric cancer is a leading cause of death worldwide, and patients have an overall 5-year survival rate of less than 10%. Using quantitative proteomic techniques together with microarray chips, we have established comprehensive proteome and transcriptome profiles of the metastatic gastric cancer TMC-1 cells and the noninvasive gastric cancer SC-M1 cell. Our qualitative protein profiling strategy offers the first comprehensive analysis of the gastric cancer cell proteome, identifying 926 and 909 proteins from SC-M1 and TMC-1 cells, respectively. Cleavable isotope-coded affinity tagging analysis allows quantitation of a total of 559 proteins (with a protein false-positive rate of <0.005), and 240 proteins were differentially expressed (>1.3-fold) between the SC-M1 and TMC-1 cells. We identified numerous proteins not previously associated with gastric cancer. Notably, a large subset of differentially expressed proteins was associated with tumor metastasis, including proteins functioning in cell-cell and cell-extracellular matrix (cell-ECM) adhesion, cell motility, proliferation, and tumor immunity. Gene expression profiling by DNA microarray revealed differential expression (of >2-fold) of about 1000 genes. The weak correlation observed between protein and mRNA profiles highlights the important complementarities of DNA microarray and proteomics approaches. These comparative data enabled us to map the disease-perturbed cell-cell and cell-ECM adhesion and Rho GTPase-mediated cytoskeletal pathways. Further validation of a subset of genes suggests the potential use of vimentin and galectin 1 as markers for metastasis. We demonstrate that combining proteomic and genomic approaches not only provides a rapid, robust, and sensitive platform to elucidate the molecular mechanisms underlying gastric cancer metastasis but also may identify candidate diagnostic markers and therapeutic targets.

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Year:  2006        PMID: 17022644     DOI: 10.1021/pr060212g

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  33 in total

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3.  Withaferin A inhibits the proliferation of gastric cancer cells by inducing G2/M cell cycle arrest and apoptosis.

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Journal:  Oncol Lett       Date:  2017-05-12       Impact factor: 2.967

Review 4.  A systems biology approach to defining metastatic biomarkers and signaling pathways.

Authors:  Natalie E Goldberger; Kent W Hunter
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2009 Jul-Aug

5.  Proteomic analysis of gastric cancer and immunoblot validation of potential biomarkers.

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Review 7.  What gastric cancer proteomic studies show about gastric carcinogenesis?

Authors:  Mariana Ferreira Leal; Fernanda Wisnieski; Carolina de Oliveira Gigek; Leonardo Caires do Santos; Danielle Queiroz Calcagno; Rommel Rodriguez Burbano; Marilia Cardoso Smith
Journal:  Tumour Biol       Date:  2016-04-28

8.  Global relationship between the proteome and transcriptome of human skeletal muscle.

Authors:  Zhengping Yi; Benjamin P Bowen; Hyonson Hwang; Christopher P Jenkinson; Dawn K Coletta; Natalie Lefort; Mandeep Bajaj; Sangeeta Kashyap; Rachele Berria; Elena A De Filippis; Lawrence J Mandarino
Journal:  J Proteome Res       Date:  2008-07-10       Impact factor: 4.466

9.  Comparison of proteomic and transcriptomic profiles in the bronchial airway epithelium of current and never smokers.

Authors:  Katrina Steiling; Aran Y Kadar; Agnes Bergerat; James Flanigon; Sriram Sridhar; Vishal Shah; Q Rushdy Ahmad; Jerome S Brody; Marc E Lenburg; Martin Steffen; Avrum Spira
Journal:  PLoS One       Date:  2009-04-09       Impact factor: 3.240

10.  KEGG spider: interpretation of genomics data in the context of the global gene metabolic network.

Authors:  Alexey V Antonov; Sabine Dietmann; Hans W Mewes
Journal:  Genome Biol       Date:  2008-12-18       Impact factor: 13.583

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