Multiplex network uncovers chronic obstructive pulmonary disease endotypes
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/346625
Tipus de documentText en actes de congrés
Data publicació2021-05
EditorBarcelona Supercomputing Center
Condicions d'accésAccés obert
Tots els drets reservats. Aquesta obra està protegida pels drets de propietat intel·lectual i
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Abstract
Chronic Obstructive Pulmonary Disease (COPD) was the
fourth leading cause of death in the world in 2019, and its
burden is projected to increase in coming decades in relation
to the aging of the population [1]. COPD is characterized
by persistent respiratory symptoms and airflow limitation.
According to the the level of airflow limitation (FEV1 %
ref.), patients are classified into four categories (GOLD groups,
Fig.1). Nevertheless, airflow severity is only one component of
COPD, as patients with the same level of airflow limitation can
present different symptoms, comorbidities and pathological
processes (i.e. emphysema, cardiovascular diseases, cachexia,
neutrophilic/eosinophilic inflammation) [2]. As a result, COPD
is currently viewed as a heterogeneous disease with several
endotypes, which are the molecular mechanisms leading to the
clinical phenotype of the disease. Recognition of this disease
heterogeneity is important as different endo-phenotypes may
respond differently to therapies, so that more personalized
therapies could be applied.
The main objective of this work is to understand the
local and molecular heterogeneity of the disease integrating
different types of genomic data which are known to play a
role in the pathology. We jointly profiled the mRNA, miRNA
and methylome in lung tissue from 135 individuals with
different grades of disease severity. In order to integrate all
the diversified data, a multiplex patient similarity network was
built and communities were detected through unsupervised
clustering. Then, these clusters of patients were characterized
using the clinical and genetic data available.
CitacióOlvera, N.; Faner, R.; Valencia, A. Multiplex network uncovers chronic obstructive pulmonary disease endotypes. A: . Barcelona Supercomputing Center, 2021, p. 81-83.
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BSC_DS-2021-30_Multiplex network uncovers.pdf | 3,867Mb | Visualitza/Obre |