Identifying chronic obstructive pulmonary disease from integrative omics and clustering in lung tissue.

ABSTRACT:

Background

Chronic obstructive pulmonary disease (COPD) is a highly morbid and heterogenous disease. While COPD is defined by spirometry, many COPD characteristics are seen in cigarette smokers with normal spirometry. The extent to which COPD and COPD heterogeneity is captured in omics of lung tissue is not known.

Methods

We clustered gene expression and methylation data in 78 lung tissue samples from former smokers with normal lung function or severe COPD. We applied two integrative omics clustering methods: (1) Similarity Network Fusion (SNF) and (2) Entropy-Based Consensus Clustering (ECC).

Results

SNF clusters were not significantly different by the percentage of COPD cases (48.8% vs. 68.6%, p = 0.13), though were different according to median forced expiratory vo

SUBMITTER: Hobbs BD 

PROVIDER: S-EPMC10091624 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature