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

Gomez-Cabrero D, Menche J, Vargas C, Cano I, Maier D, Barabási AL, Tegnér J, Roca J, Synergy-COPD Consortia

BMC Bioinformatics 17 (Suppl 15) 441 [2016-11-22; online 2016-11-22]

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. 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. 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.

Affiliated researcher

PubMed 28185567

DOI 10.1186/s12859-016-1291-3

Crossref 10.1186/s12859-016-1291-3

pii: 10.1186/s12859-016-1291-3
pmc: PMC5133493


Publications 7.1.2