Mingze Lu1, Xiaowen Fan2, Weilin Liao2, Yijiao Li3, Lijie Ma4, Mu Yuan5, Rui Gu6, Zhengdao Wei7, Chao Wang8, Hua Zhang9,10. 1. Department of Human Resources, General Hospital of Western Theater Command, Chengdu, 610083, China. 2. Department of Thoracic Surgery, General Hospital of Western Theater Command, Chengdu, 610083, China. 3. Department of Anesthesiology, The People's Hospital of Leshan, Leshan, 614000, China. 4. Department of Pulmonary and Critical Care Medicine, General Hospital of Western Theater Command, Chengdu, 610083, China. 5. Department of Scientific Research & Training, General Hospital of Western Theater Command, Chengdu, 610083, China. 6. Basic Medical Laboratory, General Hospital of Western Theater Command, Chengdu, 610083, China. 7. Department of Outpatient, General Hospital of Western Theater Command, Chengdu, 610083, China. 8. Department of Pathology, General Hospital of Western Theater Command, NO.270 Tianhui Road, Rongdu Avenue, Jinniu District, Chengdu, 610083, China. wang_chao1987@126.com. 9. Department of Pathology, General Hospital of Western Theater Command, NO.270 Tianhui Road, Rongdu Avenue, Jinniu District, Chengdu, 610083, China. zh1217104734@qq.com. 10. State Key Laboratory of Biotherapy, Sichuan University, Chengdu, 610041, China. zh1217104734@qq.com.
Abstract
BACKGROUND: Lung adenocarcinoma (LAC) is the predominant histologic subtype of lung cancer and has a complicated pathogenesis with high mortality. The purpose of this study was to identify differentially expressed genes (DEGs) with prognostic value and determine their underlying mechanisms. METHODS: Gene expression data of GSE27262 and GSE118370 were acquired from the Gene Expression Omnibus database, enrolling 31 LAC and 31 normal tissues. Common DEGs between LAC and normal tissues were identified using the GEO2R tool and Venn diagram software. Next, the Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to analyze the Gene Ontology and Kyoto Encyclopedia of Gene and Genome (KEGG) pathways. Then, protein-protein interaction (PPI) network of DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes and central genes were identified via Molecular Complex Detection. Furthermore, the expression and prognostic information of central genes were validated via Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan-Meier analysis, respectively. Finally, DAVID, real-time PCR and immunohistochemistry were applied to re-analyze the identified genes, which were also further validated in two additional datasets from ArrayExpress database. RESULTS: First, 189 common DEGs were identified among the two datasets, including 162 downregulated and 27 upregulated genes. Next, Gene Ontology and KEGG pathway analysis of the DEGs were conducted through DAVID. Then, PPI network of DEGs was constructed and 17 downregulated central genes were identified. Furthermore, the 17 downregulated central genes were validated via GEPIA and datasets from ArrayExpress, and 12 of them showed a significantly better prognosis. Finally, six genes were identified significantly enriched in neuroactive ligand-receptor interactions (EDNRB, RXFP1, P2RY1, CALCRL) and Rap1 signaling pathway (TEK, P2RY1, ANGPT1) via DAVID, which were further validated to be weakly expressed in LAC tissues via RNA quantification and immunohistochemistry analysis. CONCLUSIONS: The low expression pattern and relation to prognosis indicated that the six genes were potential tumor suppressor genes in LAC. In conclusion, we identified six significantly downregulated DEGs as prognostic markers and potential tumor suppressor genes in LAC based on integrated bioinformatics methods, which could act as potential molecular markers and therapeutic targets for LAC patients.
BACKGROUND:Lung adenocarcinoma (LAC) is the predominant histologic subtype of lung cancer and has a complicated pathogenesis with high mortality. The purpose of this study was to identify differentially expressed genes (DEGs) with prognostic value and determine their underlying mechanisms. METHODS: Gene expression data of GSE27262 and GSE118370 were acquired from the Gene Expression Omnibus database, enrolling 31 LAC and 31 normal tissues. Common DEGs between LAC and normal tissues were identified using the GEO2R tool and Venn diagram software. Next, the Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to analyze the Gene Ontology and Kyoto Encyclopedia of Gene and Genome (KEGG) pathways. Then, protein-protein interaction (PPI) network of DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes and central genes were identified via Molecular Complex Detection. Furthermore, the expression and prognostic information of central genes were validated via Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan-Meier analysis, respectively. Finally, DAVID, real-time PCR and immunohistochemistry were applied to re-analyze the identified genes, which were also further validated in two additional datasets from ArrayExpress database. RESULTS: First, 189 common DEGs were identified among the two datasets, including 162 downregulated and 27 upregulated genes. Next, Gene Ontology and KEGG pathway analysis of the DEGs were conducted through DAVID. Then, PPI network of DEGs was constructed and 17 downregulated central genes were identified. Furthermore, the 17 downregulated central genes were validated via GEPIA and datasets from ArrayExpress, and 12 of them showed a significantly better prognosis. Finally, six genes were identified significantly enriched in neuroactive ligand-receptor interactions (EDNRB, RXFP1, P2RY1, CALCRL) and Rap1 signaling pathway (TEK, P2RY1, ANGPT1) via DAVID, which were further validated to be weakly expressed in LAC tissues via RNA quantification and immunohistochemistry analysis. CONCLUSIONS: The low expression pattern and relation to prognosis indicated that the six genes were potential tumor suppressor genes in LAC. In conclusion, we identified six significantly downregulated DEGs as prognostic markers and potential tumor suppressor genes in LAC based on integrated bioinformatics methods, which could act as potential molecular markers and therapeutic targets for LAC patients.
Authors: William D Travis; Elisabeth Brambilla; Masayuki Noguchi; Andrew G Nicholson; Kim R Geisinger; Yasushi Yatabe; David G Beer; Charles A Powell; Gregory J Riely; Paul E Van Schil; Kavita Garg; John H M Austin; Hisao Asamura; Valerie W Rusch; Fred R Hirsch; Giorgio Scagliotti; Tetsuya Mitsudomi; Rudolf M Huber; Yuichi Ishikawa; James Jett; Montserrat Sanchez-Cespedes; Jean-Paul Sculier; Takashi Takahashi; Masahiro Tsuboi; Johan Vansteenkiste; Ignacio Wistuba; Pan-Chyr Yang; Denise Aberle; Christian Brambilla; Douglas Flieder; Wilbur Franklin; Adi Gazdar; Michael Gould; Philip Hasleton; Douglas Henderson; Bruce Johnson; David Johnson; Keith Kerr; Keiko Kuriyama; Jin Soo Lee; Vincent A Miller; Iver Petersen; Victor Roggli; Rafael Rosell; Nagahiro Saijo; Erik Thunnissen; Ming Tsao; David Yankelewitz Journal: J Thorac Oncol Date: 2011-02 Impact factor: 15.609