Unsupervised gene expression analyses identify IPF-severity correlated signatures, associated genes and biomarkers.

ABSTRACT:

Background

Idiopathic Pulmonary Fibrosis (IPF) is a fatal fibrotic lung disease occurring predominantly in middle-aged and older adults. The traditional diagnostic classification of IPF is based on clinical, radiological, and histopathological features. However, the considerable heterogeneity in IPF presentation suggests that differences in gene expression profiles can help to characterize and distinguish disease severity.

Methods

We used data-driven unsupervised clustering analysis, combined with a knowledge-based approach to identify and characterize IPF subgroups.

Results

Using transcriptional profiles on lung tissue from 131 patients with IPF/UIP and 12 non-diseased controls, we identified six subgroups of IPF that generally correlated with the disease severity an

SUBMITTER: Wang Y 

PROVIDER: S-EPMC5649521 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature