Multi-Omics Analysis Reveals a HIF Network and Hub Gene EPAS1 Associated with Lung Adenocarcinoma
Zhaoxi Wang,
Yongyue Wei,
Ruyang Zhang,
Li Su,
Stephanie M. Gogarten,
Geoffrey Liu,
Paul Brennan,
John K. Field,
James D. McKay,
Jolanta Lissowska,
Beata Swiatkowska,
Vladimir Janout,
Ciprian Bolca,
Milica Kontic,
Ghislaine Scelo,
David Zaridze,
Cathy C. Laurie,
Kimberly F. Doheny,
Elizabeth K. Pugh,
Beth A. Marosy,
Kurt N. Hetrick,
Xiangjun Xiao,
Claudio Pikielny,
Rayjean J. Hung,
Christopher I. Amos,
Xihong Lin,
David C. Christiani
Affiliations
Zhaoxi Wang
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
Yongyue Wei
Department of Epidemiology, Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
Ruyang Zhang
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Epidemiology, Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
Li Su
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
Stephanie M. Gogarten
Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
Geoffrey Liu
Princess Margaret Cancer Centre, Toronto, Canada
Paul Brennan
Genetic Cancer Susceptibility group, International Agency for Research on Cancer, World Health Organization, Lyon, France
John K. Field
Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
James D. McKay
Genetic Cancer Susceptibility group, International Agency for Research on Cancer, World Health Organization, Lyon, France
Jolanta Lissowska
Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie Institute - Oncology Center, Warsaw, Poland
Beata Swiatkowska
Nofer Institute of Occupational Medicine, Department of Environmental Epidemiology, Lodz, Poland
Vladimir Janout
Department of Epidemiology and Public Health, University of Ostrava, University of Olomouc, Olomouc, Czech Republic
Ciprian Bolca
Thoracic Surgery Division, ''Marius Nasta'' National Institute of Pneumology, Bucharest, Romania
Milica Kontic
Clinic of Pulmonology, Clinical Center of Serbia (KCS), Belgrade, Serbia
Ghislaine Scelo
Genetic Cancer Susceptibility group, International Agency for Research on Cancer, World Health Organization, Lyon, France
David Zaridze
Russian N.N. Blokhin Cancer Research Centre, Moscow, Russian Federation
Cathy C. Laurie
Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
Kimberly F. Doheny
Center for Inherited Disease Research, Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Elizabeth K. Pugh
Center for Inherited Disease Research, Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Beth A. Marosy
Center for Inherited Disease Research, Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Kurt N. Hetrick
Center for Inherited Disease Research, Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Xiangjun Xiao
Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
Claudio Pikielny
Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
Rayjean J. Hung
Lunenfeld-Tanenbaum Research Institute, Sinai Health System, University of Toronto, Toronto, Canada
Christopher I. Amos
Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
Xihong Lin
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
David C. Christiani
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Corresponding author at: Elkan Blout Professor of Environmental Genetics, Department of Environmental Health, Harvard TH Chan School of Public Health, 665 Huntington Avenue, Boston, MA 02115, USA.
Recent technological advancements have permitted high-throughput measurement of the human genome, epigenome, metabolome, transcriptome, and proteome at the population level. We hypothesized that subsets of genes identified from omic studies might have closely related biological functions and thus might interact directly at the network level. Therefore, we conducted an integrative analysis of multi-omic datasets of non-small cell lung cancer (NSCLC) to search for association patterns beyond the genome and transcriptome. A large, complex, and robust gene network containing well-known lung cancer-related genes, including EGFR and TERT, was identified from combined gene lists for lung adenocarcinoma. Members of the hypoxia-inducible factor (HIF) gene family were at the center of this network. Subsequent sequencing of network hub genes within a subset of samples from the Transdisciplinary Research in Cancer of the Lung-International Lung Cancer Consortium (TRICL-ILCCO) consortium revealed a SNP (rs12614710) in EPAS1 associated with NSCLC that reached genome-wide significance (OR = 1.50; 95% CI: 1.31–1.72; p = 7.75 × 10−9). Using imputed data, we found that this SNP remained significant in the entire TRICL-ILCCO consortium (p = .03). Additional functional studies are warranted to better understand interrelationships among genetic polymorphisms, DNA methylation status, and EPAS1 expression. Keywords: Non-small cell lung cancer, Lung adenocarcinoma, Integrated analysis, Network analysis, Hypoxia-inducible factor
