Gomez-Cabrero, DavidMenche, JörgVargas, ClaudiaCano, IsaacMaier, DieterBarabasi, Albert-LaszloTegnér, JesperRoca, Josep2017-01-032016Gomez-Cabrero, David, Jörg Menche, Claudia Vargas, Isaac Cano, Dieter Maier, Albert-László Barabási, Jesper Tegnér, and Josep Roca. 2016. “From comorbidities of chronic obstructive pulmonary disease to identification of shared molecular mechanisms by data integration.” BMC Bioinformatics 17 (Suppl 15): 23-35. doi:10.1186/s12859-016-1291-3. http://dx.doi.org/10.1186/s12859-016-1291-3.1471-2105http://nrs.harvard.edu/urn-3:HUL.InstRepos:29739013Background: Deep 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. Results: Since 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. Conclusions: The 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. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1291-3) contains supplementary material, which is available to authorized users.en-USJournal Article2017-01-0310.1186/s12859-016-1291-3