Identification of common signatures in idiopathic pulmonary fibrosis and lung cancer using gene expression modeling.

ABSTRACT:

Background

Idiopathic pulmonary fibrosis (IPF) is associated with an increased risk for lung cancer, but the underlying mechanisms driving malignant transformation remain largely unknown. This study aimed to identify differentially expressed genes (DEGs) distinguishing IPF and lung cancer from healthy individuals and common genes driving the transformation from healthy to IPF and lung cancer.

Methods

The gene expression data for IPF and non-small cell lung cancer (NSCLC) were retrieved from the Gene Expression Omnibus (GEO) database. The DEG signatures were identified via unsupervised two-way clustering (TWC) analysis, supervised support vector machine analysis, dimensional reduction, and mutual exclusivity analysis. Gene enrichment and pathway analyses were performed to ide

SUBMITTER: Leng D 

PROVIDER: S-EPMC7552373 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature