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Title: From comorbidities of chronic obstructive pulmonary disease to identification of shared molecular mechanisms by data integration
Author: Gomez Cabrero, David
Menche, Jörg
Vargas, Claudia
Cano Franco, Isaac
Maier, Dieter
Barabási, Albert László
Tegnér, Jesper
Roca Torrent, Josep
Synergy‐COPD consortium
Keywords: Malalties pulmonars obstructives cròniques
Mineria de dades
Marcadors bioquímics
Malalties de l'aparell digestiu
Chronic obstructive pulmonary diseases
Data mining
Biochemical markers
Digestive system diseases
Issue Date: 22-Nov-2016
Publisher: BioMed Central
Abstract: Background: 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
Note: Reproducció del document publicat a:
It is part of: BMC Bioinformatics, 2016, vol. 17, num. Suppl 15, p. 441
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ISSN: 1471-2105
Appears in Collections:Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer)
Articles publicats en revistes (Medicina)
Publicacions de projectes de recerca finançats per la UE

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