Identification of PANoptosis-related genes for idiopathic pulmonary fibrosis by machine learning and molecular subtype analysis

Abstract

Abstract Idiopathic pulmonary fibrosis (IPF) is a severe interstitial lung disease characterized by a grim prognosis, in which various forms of cell death are significant contributors to its development. The objective of this study is to explore diagnostic biomarkers and molecular subtypes associated with PANoptosis in IPF, and to develop reliable diagnostic models based on PANoptosis-related mechanisms. The peripheral blood transcriptomic data of IPF from the Gene Expression Omnibus (GEO) database and PANoptosis-related genes from the GeneCards database were utilized to conduct differential gene expression analysis and weighted gene co-expression network analysis (WGCNA), identifying PANoptosis-related differentially expressed genes (PDEGs). We yielded 9 PDEGs and employed machine learning algorithms to identify 3 key diagnostic biomarkers for PANoptosis in IPF: MMP9, FCMR, NIBAN3. Consensus clustering algorithm was applied to recognize two PANoptosis-related subtypes. Cluster 1 exhibited higher abundance of adaptive immune response cells and significant enrichment in DNA damage and repair-related pathways. Cluster 2 exhibited greater prevalence of innate immune response cells and predominant enhancement in pathways related to lipid cholesterol metabolism and vascular remodeling. Diagnostic models were developed with the aid of clinical decision-making and a novel approach to the diagnosis and treatment for IPF.

Keywords

• PANoptosis
• Idiopathic pulmonary fibrosis
• Machine learning
• Molecular subtype