Literature DB >> 18563251

Identification of pathways associated with invasive behavior by ovarian cancer cells using multidimensional protein identification technology (MudPIT).

Katharine L Sodek1, Andreas I Evangelou, Alex Ignatchenko, Mahima Agochiya, Theodore J Brown, Maurice J Ringuette, Igor Jurisica, Thomas Kislinger.   

Abstract

Proteomic profiling has emerged as a useful tool for identifying tissue alterations in disease states including malignant transformation. The aim of this study was to reveal expression profiles associated with the highly motile/invasive ovarian cancer cell phenotype. Six ovarian cancer cell lines were subjected to proteomic characterization using multidimensional protein identification technology (MudPIT), and evaluated for their motile/invasive behavior, so that these parameters could be compared. Within whole cell extracts of the ovarian cancer cells, MudPIT identified proteins that mapped to 2245 unique genes. Western blot analysis for selected proteins confirmed the expression profiles revealed by MudPIT, demonstrating the fidelity of this high-throughput analysis. Unsupervised cluster analysis partitioned the cell lines in a manner that reflected their motile/invasive capacity. A comparison of protein expression profiles between cell lines of high (group 1) versus low (group 2) motile/invasive capacity revealed 300 proteins that were differentially expressed, of which 196 proteins were significantly upregulated in group 1. Protein network and KEGG pathway analysis indicated a functional interplay between proteins up-regulated in group 1 cells, with increased expression of several key members of the actin cytoskeleton, extracellular matrix (ECM) and focal adhesion pathways. These proteomic expression profiles can be utilized to distinguish highly motile, aggressive ovarian cancer cells from lesser invasive ones, and could prove to be essential in the development of more effective strategies that target pivotal cell signaling pathways used by cancer cells during local invasion and distant metastasis.

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Year:  2008        PMID: 18563251     DOI: 10.1039/b717542f

Source DB:  PubMed          Journal:  Mol Biosyst        ISSN: 1742-2051


  29 in total

1.  Loss of the Timp gene family is sufficient for the acquisition of the CAF-like cell state.

Authors:  Masayuki Shimoda; Simona Principe; Hartland W Jackson; Valbona Luga; Hui Fang; Sam D Molyneux; Yang W Shao; Alison Aiken; Paul D Waterhouse; Christina Karamboulas; Franz M Hess; Takashi Ohtsuka; Yasunori Okada; Laurie Ailles; Andreas Ludwig; Jeffrey L Wrana; Thomas Kislinger; Rama Khokha
Journal:  Nat Cell Biol       Date:  2014-08-24       Impact factor: 28.824

2.  Translational analysis of mouse and human placental protein and mRNA reveals distinct molecular pathologies in human preeclampsia.

Authors:  Brian Cox; Parveen Sharma; Andreas I Evangelou; Kathie Whiteley; Vladimir Ignatchenko; Alex Ignatchenko; Dora Baczyk; Marie Czikk; John Kingdom; Janet Rossant; Anthony O Gramolini; S Lee Adamson; Thomas Kislinger
Journal:  Mol Cell Proteomics       Date:  2011-10-10       Impact factor: 5.911

3.  Quantitative proteomic profiling studies of pancreatic cancer stem cells.

Authors:  Lan Dai; Chen Li; Kerby A Shedden; Cheong J Lee; Chenwei Li; HuyVuong Quoc; Diane M Simeone; David M Lubman
Journal:  J Proteome Res       Date:  2010-07-02       Impact factor: 4.466

4.  Integrated omic analysis of oropharyngeal carcinomas reveals human papillomavirus (HPV)-dependent regulation of the activator protein 1 (AP-1) pathway.

Authors:  Lusia Sepiashvili; Daryl Waggott; Angela Hui; Wei Shi; Susie Su; Alex Ignatchenko; Vladimir Ignatchenko; Marissa Laureano; Shao Hui Huang; Wei Xu; Ilan Weinreb; John Waldron; Brian O'Sullivan; Jonathan C Irish; Paul C Boutros; Fei-Fei Liu; Thomas Kislinger
Journal:  Mol Cell Proteomics       Date:  2014-09-30       Impact factor: 5.911

Review 5.  Proteomics and ovarian cancer: integrating proteomics information into clinical care.

Authors:  John L Hays; Geoffrey Kim; Iulia Giuroiu; Elise C Kohn
Journal:  J Proteomics       Date:  2010-06-01       Impact factor: 4.044

6.  Identification of candidate biomarkers with cancer-specific glycosylation in the tissue and serum of endometrioid ovarian cancer patients by glycoproteomic analysis.

Authors:  Karen L Abbott; Jae-Min Lim; Lance Wells; Benedict B Benigno; John F McDonald; Michael Pierce
Journal:  Proteomics       Date:  2010-02       Impact factor: 3.984

7.  A human functional protein interaction network and its application to cancer data analysis.

Authors:  Guanming Wu; Xin Feng; Lincoln Stein
Journal:  Genome Biol       Date:  2010-05-19       Impact factor: 13.583

8.  Potentially novel candidate biomarkers for head and neck squamous cell carcinoma identified using an integrated cell line-based discovery strategy.

Authors:  Lusia Sepiashvili; Angela Hui; Vladimir Ignatchenko; Willa Shi; Susie Su; Wei Xu; Shao Hui Huang; Brian O'Sullivan; John Waldron; Jonathan C Irish; Bayardo Perez-Ordonez; Fei-Fei Liu; Thomas Kislinger
Journal:  Mol Cell Proteomics       Date:  2012-08-23       Impact factor: 5.911

9.  Identification of differentially expressed proteins in direct expressed prostatic secretions of men with organ-confined versus extracapsular prostate cancer.

Authors:  Yunee Kim; Vladimir Ignatchenko; Cindy Q Yao; Irina Kalatskaya; Julius O Nyalwidhe; Raymond S Lance; Anthony O Gramolini; Dean A Troyer; Lincoln D Stein; Paul C Boutros; Jeffrey A Medin; O John Semmes; Richard R Drake; Thomas Kislinger
Journal:  Mol Cell Proteomics       Date:  2012-09-17       Impact factor: 5.911

10.  Comparative systems biology of human and mouse as a tool to guide the modeling of human placental pathology.

Authors:  Brian Cox; Max Kotlyar; Andreas I Evangelou; Vladimir Ignatchenko; Alex Ignatchenko; Kathie Whiteley; Igor Jurisica; S Lee Adamson; Janet Rossant; Thomas Kislinger
Journal:  Mol Syst Biol       Date:  2009-06-16       Impact factor: 11.429

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