Literature DB >> 20426488

Quantitative proteomics analysis reveals molecular networks regulated by epidermal growth factor receptor level in head and neck cancer.

Wei Yang1, Quan Cai, Vivian W Y Lui, Patrick A Everley, Jayoung Kim, Neil Bhola, Kelly M Quesnelle, Bruce R Zetter, Hanno Steen, Michael R Freeman, Jennifer R Grandis.   

Abstract

Epidermal growth factor receptor (EGFR) is overexpressed in up to 90% of head and neck cancer (HNC), where increased expression levels of EGFR correlate with poor prognosis. To date, EGFR expression levels have not predicted the clinical response to the EGFR-targeting therapies. Elucidation of the molecular mechanisms underlying anti-EGFR-induced antitumor effects may shed some light on the mechanisms of HNC resistance to EGFR-targeting therapeutics and provide novel targets for improving the treatment of HNC. Here, we conducted a quantitative proteomics analysis to determine the molecular networks regulated by EGFR levels in HNC by specifically knocking-down EGFR and employing stable isotope labeling with amino acids in cell culture (SILAC). Following data normalization to minimize systematic errors and Western blotting validation, 12 proteins (e.g., p21, stratifin, and maspin) and 24 proteins (e.g., cdc2 and MTA2) were found to be significantly upregulated or downregulated by EGFR knockdown, respectively. Bioinformatic analysis revealed that these proteins were mainly involved in long-chain fatty acid biosynthesis and beta-oxidation, cholesterol biosynthesis, cell proliferation, DNA replication, and apoptosis. Cell cycle analysis confirmed that G(2)/M phase progression was significantly inhibited by EGFR knockdown, a hypothesis generated from network modeling. Further investigation of these molecular networks may not only enhance our understanding of the antitumor mechanisms of EGFR targeting but also improve patient selection and provide novel targets for better therapeutics.

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Year:  2010        PMID: 20426488      PMCID: PMC3149825          DOI: 10.1021/pr901211j

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


  67 in total

1.  Deacetylation of p53 modulates its effect on cell growth and apoptosis.

Authors:  J Luo; F Su; D Chen; A Shiloh; W Gu
Journal:  Nature       Date:  2000-11-16       Impact factor: 49.962

Review 2.  Proteomic approaches to the analysis of multiprotein signaling complexes.

Authors:  Wei Yang; Hanno Steen; Michael R Freeman
Journal:  Proteomics       Date:  2008-02       Impact factor: 3.984

3.  A model for p53-induced apoptosis.

Authors:  K Polyak; Y Xia; J L Zweier; K W Kinzler; B Vogelstein
Journal:  Nature       Date:  1997-09-18       Impact factor: 49.962

4.  Identification of the human PHLDA1/TDAG51 gene: down-regulation in metastatic melanoma contributes to apoptosis resistance and growth deregulation.

Authors:  Rüdiger Neef; Martina A Kuske; Elma Pröls; Judith P Johnson
Journal:  Cancer Res       Date:  2002-10-15       Impact factor: 12.701

5.  Antitumor mechanisms of combined gastrin-releasing peptide receptor and epidermal growth factor receptor targeting in head and neck cancer.

Authors:  Qing Zhang; Neil E Bhola; Vivian Wai Yan Lui; Doris R Siwak; Sufi M Thomas; Christopher T Gubish; Jill M Siegfried; Gordon B Mills; Dong Shin; Jennifer Rubin Grandis
Journal:  Mol Cancer Ther       Date:  2007-04       Impact factor: 6.261

6.  Maspin polymorphism associated with apoptosis susceptibility and in vivo tumorigenesis.

Authors:  Hye-Lim Jang; Eunsook Nam; Kon Ho Lee; Seonyong Yeom; Hee Jung Son; Chaehwa Park
Journal:  Int J Mol Med       Date:  2008-09       Impact factor: 4.101

7.  Silencing MAT2A gene by RNA interference inhibited cell growth and induced apoptosis in human hepatoma cells.

Authors:  Quanyan Liu; Kailang Wu; Ying Zhu; Yueming He; Jianguo Wu; Zhisu Liu
Journal:  Hepatol Res       Date:  2007-05       Impact factor: 4.288

8.  Quantitative proteomic signature of liver cancer cells: tissue transglutaminase 2 could be a novel protein candidate of human hepatocellular carcinoma.

Authors:  Yulin Sun; Wei Mi; Jianqiang Cai; Wantao Ying; Fang Liu; Haizhen Lu; Yuanyuan Qiao; Wei Jia; Xinyu Bi; Ning Lu; Shangmei Liu; Xiaohong Qian; Xiaohang Zhao
Journal:  J Proteome Res       Date:  2008-07-23       Impact factor: 4.466

9.  A computational approach to correct arginine-to-proline conversion in quantitative proteomics.

Authors:  Sung Kyu Park; Lujian Liao; Jin Young Kim; John R Yates
Journal:  Nat Methods       Date:  2009-03       Impact factor: 28.547

10.  Dissection of the insulin signaling pathway via quantitative phosphoproteomics.

Authors:  Marcus Krüger; Irina Kratchmarova; Blagoy Blagoev; Yu-Hua Tseng; C Ronald Kahn; Matthias Mann
Journal:  Proc Natl Acad Sci U S A       Date:  2008-02-11       Impact factor: 11.205

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  12 in total

1.  msCompare: a framework for quantitative analysis of label-free LC-MS data for comparative candidate biomarker studies.

Authors:  Berend Hoekman; Rainer Breitling; Frank Suits; Rainer Bischoff; Peter Horvatovich
Journal:  Mol Cell Proteomics       Date:  2012-02-07       Impact factor: 5.911

Review 2.  Advancement of mass spectrometry-based proteomics technologies to explore triple negative breast cancer.

Authors:  Sayem Miah; Charles A S Banks; Mark K Adams; Laurence Florens; Kiven E Lukong; Michael P Washburn
Journal:  Mol Biosyst       Date:  2016-12-20

3.  Integrated Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) and Isobaric Tags for Relative and Absolute Quantitation (iTRAQ) Quantitative Proteomic Analysis Identifies Galectin-1 as a Potential Biomarker for Predicting Sorafenib Resistance in Liver Cancer.

Authors:  Chao-Chi Yeh; Chih-Hung Hsu; Yu-Yun Shao; Wen-Ching Ho; Mong-Hsun Tsai; Wen-Chi Feng; Lu-Ping Chow
Journal:  Mol Cell Proteomics       Date:  2015-04-07       Impact factor: 5.911

4.  Quantitative proteomics identifies a beta-catenin network as an element of the signaling response to Frizzled-8 protein-related antiproliferative factor.

Authors:  Wei Yang; Yeun Goo Chung; Yongsoo Kim; Taek-Kyun Kim; Susan K Keay; Chen-Ou Zhang; Mihee Ji; Daehee Hwang; Kwang Pyo Kim; Hanno Steen; Michael R Freeman; Jayoung Kim
Journal:  Mol Cell Proteomics       Date:  2011-03-21       Impact factor: 5.911

5.  In-depth proteomics characterization of ∆Np73 effectors identifies key proteins with diagnostic potential implicated in lymphangiogenesis, vasculogenesis and metastasis in colorectal cancer.

Authors:  María Garranzo-Asensio; Javier Rodríguez-Cobos; Coral San Millán; Carmen Poves; María Jesús Fernández-Aceñero; Daniel Pastor-Morate; David Viñal; Ana Montero-Calle; Guillermo Solís-Fernández; María-Ángeles Ceron; Manuel Gámez-Chiachio; Nuria Rodríguez; Ana Guzmán-Aránguez; Rodrigo Barderas; Gemma Domínguez
Journal:  Mol Oncol       Date:  2022-06-07       Impact factor: 7.449

6.  In-depth characterization of the secretome of colorectal cancer metastatic cells identifies key proteins in cell adhesion, migration, and invasion.

Authors:  Rodrigo Barderas; Marta Mendes; Sofia Torres; Rubén A Bartolomé; María López-Lucendo; Roi Villar-Vázquez; Alberto Peláez-García; Eduardo Fuente; Félix Bonilla; J Ignacio Casal
Journal:  Mol Cell Proteomics       Date:  2013-02-26       Impact factor: 5.911

7.  Identification of differentially expressed proteins in the locoregional recurrent esophageal squamous cell carcinoma by quantitative proteomics.

Authors:  Wei-Wei Yu; Xiao-Long Fu; Xu-Wei Cai; Meng-Hong Sun; Yan-Mei Guo
Journal:  J Gastrointest Oncol       Date:  2021-06

Review 8.  Combining histone deacetylase inhibitors (HDACis) with other therapies for cancer therapy.

Authors:  Mengjiao Zhou; Minjian Yuan; Meng Zhang; Chenyi Lei; Omer Aras; Xiaohong Zhang; Feifei An
Journal:  Eur J Med Chem       Date:  2021-09-04       Impact factor: 7.088

9.  Regulation of microtubule dynamics by DIAPH3 influences amoeboid tumor cell mechanics and sensitivity to taxanes.

Authors:  Samantha Morley; Sungyong You; Sara Pollan; Jiyoung Choi; Bo Zhou; Martin H Hager; Kenneth Steadman; Cristiana Spinelli; Kavitha Rajendran; Arkadiusz Gertych; Jayoung Kim; Rosalyn M Adam; Wei Yang; Ramaswamy Krishnan; Beatrice S Knudsen; Dolores Di Vizio; Michael R Freeman
Journal:  Sci Rep       Date:  2015-07-16       Impact factor: 4.379

10.  Integration of proteomic and transcriptomic profiles identifies a novel PDGF-MYC network in human smooth muscle cells.

Authors:  Wei Yang; Aruna Ramachandran; Sungyong You; HyoBin Jeong; Samantha Morley; Michelle D Mulone; Tanya Logvinenko; Jayoung Kim; Daehee Hwang; Michael R Freeman; Rosalyn M Adam
Journal:  Cell Commun Signal       Date:  2014-08-01       Impact factor: 5.712

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