From comorbidities of chronic obstructive pulmonary disease to identification of shared molecular mechanisms by data integration

BMC Bioinformatics

Research

Dieter Maier¹  Jörg Menche²  Albert-László Barabási³  Jesper Tegnér⁴  David Gomez-Cabrero⁵  Claudia Vargas⁶  Isaac Cano⁶  Josep Roca⁶ 

[1] Biomax Informatics AG, Planegg, Germany;Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA, USA;Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA;Center for Network Science, Central European University, Budapest, Hungary;Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA, USA;Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA;Center for Network Science, Central European University, Budapest, Hungary;Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA;Department of Medicine, Karolinska Institutet, Unit of Computational Medicine, 171 77, Stockholm, Sweden;Karolinska Institutet, Center for Molecular Medicine, 171 77, Stockholm, Sweden;Department of Medicine, Unit of Clinical Epidemiology, Karolinska University Hospital, L8, 17176, Solna, Sweden;Science for Life Laboratory, 17121, Solna, Sweden;Department of Medicine, Karolinska Institutet, Unit of Computational Medicine, 171 77, Stockholm, Sweden;Karolinska Institutet, Center for Molecular Medicine, 171 77, Stockholm, Sweden;Department of Medicine, Unit of Clinical Epidemiology, Karolinska University Hospital, L8, 17176, Solna, Sweden;Science for Life Laboratory, 17121, Solna, Sweden;Mucosal and Salivary Biology Division, King’s College London Dental Institute, London, UK;Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clinic de Barcelona, Universitat de Barcelona, Barcelona, Spain;Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Madrid, Spain;

关键词: Chronic Obstructive Pulmonary Disease;    Celiac Disease;    Human Leukocyte Antigen;    Chronic Obstructive Pulmonary Disease Patient;    ICD9 Code;   

DOI  :  10.1186/s12859-016-1291-3

来源: Springer

【 摘 要 】

BackgroundDeep mining of healthcare data has provided maps of comorbidity relationships between diseases. In parallel, integrative multi-omics investigations have generated high-resolution molecular maps of putative relevance for understanding disease initiation and progression. Yet, it is unclear how to advance an observation of comorbidity relations (one disease to others) to a molecular understanding of the driver processes and associated biomarkers.ResultsSince Chronic Obstructive Pulmonary disease (COPD) has emerged as a central hub in temporal comorbidity networks, we developed a systematic integrative data-driven framework to identify shared disease-associated genes and pathways, as a proxy for the underlying generative mechanisms inducing comorbidity. We integrated records from approximately 13 M patients from the Medicare database with disease-gene maps that we derived from several resources including a semantic-derived knowledge-base. Using rank-based statistics we not only recovered known comorbidities but also discovered a novel association between COPD and digestive diseases. Furthermore, our analysis provides the first set of COPD co-morbidity candidate biomarkers, including IL15, TNF and JUP, and characterizes their association to aging and life-style conditions, such as smoking and physical activity.ConclusionsThe developed framework provides novel insights in COPD and especially COPD co-morbidity associated mechanisms. The methodology could be used to discover and decipher the molecular underpinning of other comorbidity relationships and furthermore, allow the identification of candidate co-morbidity biomarkers.

【 授权许可】

CC BY   
© The Author(s). 2016