From Comorbidities of Chronic Obstructive Pulmonary Disease to Identification of Shared Molecular Mechanisms by Data Integration

D. Gomez-Cabrero, J. Menche, C. Vargas, I. Cano, D. Maier, A.-L. Barabasi, J. Tegner, J. Roca, and on Behalf of Synergy-COPD Consortia.
BMC Bioinformatics
17: 1291 (2016).
November 22, 2016

Abstract

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.

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