BACKGROUND: The aim of this study was to investigate the gene expression profile of chronic obstructive pulmonary disease (COPD) patients and non-COPD patients. METHODS: Microarray raw data (GSE29133) was downloaded from Gene Expression Omnibus, including three COPD samples and three normal controls. Gene expression profiling was performed using Affymetrix human genome u133 plus 2.0 GeneChip. Differentially expressed genes were identified by Student's t test and genes with p < 0.05 were considered significantly changed. Up- and downregulated genes were submitted to the molecular signatures database (MSigDB) to search for a possible association with other previously published gene expression signatures. Furthermore, we constructed a COPD protein-protein interaction (PPI) network and used the connectivity map (cMap) to query for potential drugs for COPD. RESULTS: A total of 680 upregulated genes and 530 downregulated genes in COPD were identified. The MSigDB investigation found that upregulated genes were highly similar to gene signatures that respond to interferon and downregulated genes were similar to erythroid progenitor cells from fetal livers of E13.5 embryos with KLF1 knocked out. A PPI network consisting of 814 gene/proteins and 2,613 interactions was identified by Search Tool for the Retrieval of Interacting Genes. The cMap predicted helveticoside, disulfiram, and lanatoside C as the top three possible drugs that could perhaps treat COPD. CONCLUSION: Comprehensive analysis of the gene expression profile for COPD versus control reveals helveticoside, disulfiram, and lanatoside C as potential molecular targets in COPD. This evidence provides a new breakthrough in the medical treatment of patients with COPD.
BACKGROUND: The aim of this study was to investigate the gene expression profile of chronic obstructive pulmonary disease (COPD) patients and non-COPDpatients. METHODS: Microarray raw data (GSE29133) was downloaded from Gene Expression Omnibus, including three COPD samples and three normal controls. Gene expression profiling was performed using Affymetrix human genome u133 plus 2.0 GeneChip. Differentially expressed genes were identified by Student's t test and genes with p < 0.05 were considered significantly changed. Up- and downregulated genes were submitted to the molecular signatures database (MSigDB) to search for a possible association with other previously published gene expression signatures. Furthermore, we constructed a COPD protein-protein interaction (PPI) network and used the connectivity map (cMap) to query for potential drugs for COPD. RESULTS: A total of 680 upregulated genes and 530 downregulated genes in COPD were identified. The MSigDB investigation found that upregulated genes were highly similar to gene signatures that respond to interferon and downregulated genes were similar to erythroid progenitor cells from fetal livers of E13.5 embryos with KLF1 knocked out. A PPI network consisting of 814 gene/proteins and 2,613 interactions was identified by Search Tool for the Retrieval of Interacting Genes. The cMap predicted helveticoside, disulfiram, and lanatoside C as the top three possible drugs that could perhaps treat COPD. CONCLUSION: Comprehensive analysis of the gene expression profile for COPD versus control reveals helveticoside, disulfiram, and lanatoside C as potential molecular targets in COPD. This evidence provides a new breakthrough in the medical treatment of patients with COPD.
Authors: Paul Shannon; Andrew Markiel; Owen Ozier; Nitin S Baliga; Jonathan T Wang; Daniel Ramage; Nada Amin; Benno Schwikowski; Trey Ideker Journal: Genome Res Date: 2003-11 Impact factor: 9.043
Authors: Jianqing Lin; Michael C Haffner; Yonggang Zhang; Byron H Lee; W Nathaniel Brennen; Justin Britton; Sushant K Kachhap; Joong Sup Shim; Jun O Liu; William G Nelson; Srinivasan Yegnasubramanian; Michael A Carducci Journal: Prostate Date: 2010-08-31 Impact factor: 4.104
Authors: Andre M Pilon; Murat O Arcasoy; Holly K Dressman; Serena E Vayda; Yelena D Maksimova; Jose I Sangerman; Patrick G Gallagher; David M Bodine Journal: Mol Cell Biol Date: 2008-10-13 Impact factor: 4.272
Authors: Kristiina Iljin; Kirsi Ketola; Paula Vainio; Pasi Halonen; Pekka Kohonen; Vidal Fey; Roland C Grafström; Merja Perälä; Olli Kallioniemi Journal: Clin Cancer Res Date: 2009-09-29 Impact factor: 12.531
Authors: Wen Ning; Chao-Jun Li; Naftali Kaminski; Carol A Feghali-Bostwick; Sean M Alber; Yuanpu P Di; Sherrie L Otterbein; Ruiping Song; Shizu Hayashi; Zhihong Zhou; David J Pinsky; Simon C Watkins; Joseph M Pilewski; Frank C Sciurba; David G Peters; James C Hogg; Augustine M K Choi Journal: Proc Natl Acad Sci U S A Date: 2004-10-05 Impact factor: 11.205
Authors: David Gomez-Cabrero; Jörg Menche; Isaac Cano; Imad Abugessaisa; Mercedes Huertas-Migueláñez; Akos Tenyi; Igor Marin de Mas; Narsis A Kiani; Francesco Marabita; Francesco Falciani; Kelly Burrowes; Dieter Maier; Peter Wagner; Vitaly Selivanov; Marta Cascante; Josep Roca; Albert-László Barabási; Jesper Tegnér Journal: J Transl Med Date: 2014-11-28 Impact factor: 5.531