Constructing an extracellular matrix-related prognostic model for idiopathic pulmonary fibrosis based on machine learning.

ABSTRACT:

Background

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive interstitial lung disease. Multiple research has revealed that the extracellular matrix (ECM) may be associated with the development and prognosis of IPF, however, the underlying mechanisms remain incompletely understood.

Methods

We included GSE70866 dataset from the GEO database and established an ECM-related prognostic model utilizing LASSO, Random forest and Support vector machines algorithms. To compare immune cell infiltration levels between the high and low risk groups, we employed the ssGSEA algorithm. Enrichment analysis was conducted to explore pathway differences between the high-risk and low-risk groups. Finally, the model genes were validated using an external validation set consisting of

SUBMITTER: Luo H 

PROVIDER: S-EPMC10585847 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature