X Wu1, W Zang, S Cui, M Wang. 1. Department of Cardiothoracic Surgery, Shanghai 10th People's Hospital, Shanghai, China.
Abstract
BACKGROUND: Lung adenocarcinoma (LAC) is the most frequent histologic type of lung cancer and rates of adenocarcinoma are increasing in most countries. Recently, several molecular markers have been identified to predict LAC. However, more prognostic makers and the underlying role of those makers are still imperative. AIM: In this study, our objective was to identify a set of discriminating genes that can be used for characterization and prediction of response to LAC. MATERIALS AND METHODS: Using the bioinformatics analysis method, we merged two LAC datasets-GSE2514 and GSE7670 to find novel target genes and pathways to explain the pathogenicity. RESULTS: The results showed that EDNRB (endothelin receptor type B), ADRB2 (beta-adrenergic receptor), S1PR1 (sphingosine-1-phosphate receptor 1), P2RY14 (PsY purinoceptor 14), LEPR (leptin-receptor), GHR (growth hormone receptor), PPM1D (protein phosphatase-1D), and GADD45B (growth arrest and DNA-damage-inducible, beta) have high degrees in response to LAC. Additionally, EDNRB, ADRB2, S1PR1, P2RY14, LEPR, and GHR may be involved in LAC through Neuroactive ligand-receptor interaction, but PPM1D and GADD45B may be through p53 signaling pathway. Some of our prediction had been demonstrated by previous reports, such as ADRB2, S1PR1, GHR, PPM1D, and GADD45B. Therefore, we hope our study could lay a basis for further study of other target genes, such as EDNRB, P2RY14, and LEPR. CONCLUSIONS: It is effective to identify potential molecular marker for LAC and predict their underlying functions by bioinformatics analysis and graph clustering method. However, further experiments are still indispensable to confirm our conclusion.
BACKGROUND:Lung adenocarcinoma (LAC) is the most frequent histologic type of lung cancer and rates of adenocarcinoma are increasing in most countries. Recently, several molecular markers have been identified to predict LAC. However, more prognostic makers and the underlying role of those makers are still imperative. AIM: In this study, our objective was to identify a set of discriminating genes that can be used for characterization and prediction of response to LAC. MATERIALS AND METHODS: Using the bioinformatics analysis method, we merged two LAC datasets-GSE2514 and GSE7670 to find novel target genes and pathways to explain the pathogenicity. RESULTS: The results showed that EDNRB (endothelin receptor type B), ADRB2 (beta-adrenergic receptor), S1PR1 (sphingosine-1-phosphate receptor 1), P2RY14 (PsY purinoceptor 14), LEPR (leptin-receptor), GHR (growth hormone receptor), PPM1D (protein phosphatase-1D), and GADD45B (growth arrest and DNA-damage-inducible, beta) have high degrees in response to LAC. Additionally, EDNRB, ADRB2, S1PR1, P2RY14, LEPR, and GHR may be involved in LAC through Neuroactive ligand-receptor interaction, but PPM1D and GADD45B may be through p53 signaling pathway. Some of our prediction had been demonstrated by previous reports, such as ADRB2, S1PR1, GHR, PPM1D, and GADD45B. Therefore, we hope our study could lay a basis for further study of other target genes, such as EDNRB, P2RY14, and LEPR. CONCLUSIONS: It is effective to identify potential molecular marker for LAC and predict their underlying functions by bioinformatics analysis and graph clustering method. However, further experiments are still indispensable to confirm our conclusion.
Authors: Filipe L F Carvalho; Luigi Marchionni; Anuj Gupta; Basheer A Kummangal; Edward M Schaeffer; Ashley E Ross; David M Berman Journal: J Cell Mol Med Date: 2015-04-12 Impact factor: 5.310
Authors: Xuemei Ji; Yohan Bossé; Maria Teresa Landi; Jiang Gui; Xiangjun Xiao; David Qian; Philippe Joubert; Maxime Lamontagne; Yafang Li; Ivan Gorlov; Mariella de Biasi; Younghun Han; Olga Gorlova; Rayjean J Hung; Xifeng Wu; James McKay; Xuchen Zong; Robert Carreras-Torres; David C Christiani; Neil Caporaso; Mattias Johansson; Geoffrey Liu; Stig E Bojesen; Loic Le Marchand; Demetrios Albanes; Heike Bickeböller; Melinda C Aldrich; William S Bush; Adonina Tardon; Gad Rennert; Chu Chen; M Dawn Teare; John K Field; Lambertus A Kiemeney; Philip Lazarus; Aage Haugen; Stephen Lam; Matthew B Schabath; Angeline S Andrew; Hongbing Shen; Yun-Chul Hong; Jian-Min Yuan; Pier A Bertazzi; Angela C Pesatori; Yuanqing Ye; Nancy Diao; Li Su; Ruyang Zhang; Yonathan Brhane; Natasha Leighl; Jakob S Johansen; Anders Mellemgaard; Walid Saliba; Christopher Haiman; Lynne Wilkens; Ana Fernandez-Somoano; Guillermo Fernandez-Tardon; Erik H F M van der Heijden; Jin Hee Kim; Juncheng Dai; Zhibin Hu; Michael P A Davies; Michael W Marcus; Hans Brunnström; Jonas Manjer; Olle Melander; David C Muller; Kim Overvad; Antonia Trichopoulou; Rosario Tumino; Jennifer Doherty; Gary E Goodman; Angela Cox; Fiona Taylor; Penella Woll; Irene Brüske; Judith Manz; Thomas Muley; Angela Risch; Albert Rosenberger; Kjell Grankvist; Mikael Johansson; Frances Shepherd; Ming-Sound Tsao; Susanne M Arnold; Eric B Haura; Ciprian Bolca; Ivana Holcatova; Vladimir Janout; Milica Kontic; Jolanta Lissowska; Anush Mukeria; Simona Ognjanovic; Tadeusz M Orlowski; Ghislaine Scelo; Beata Swiatkowska; David Zaridze; Per Bakke; Vidar Skaug; Shanbeh Zienolddiny; Eric J Duell; Lesley M Butler; Woon-Puay Koh; Yu-Tang Gao; Richard Houlston; John McLaughlin; Victoria Stevens; David C Nickle; Ma'en Obeidat; Wim Timens; Bin Zhu; Lei Song; María Soler Artigas; Martin D Tobin; Louise V Wain; Fangyi Gu; Jinyoung Byun; Ahsan Kamal; Dakai Zhu; Rachel F Tyndale; Wei-Qi Wei; Stephen Chanock; Paul Brennan; Christopher I Amos Journal: Nat Commun Date: 2018-08-13 Impact factor: 14.919