Prognostic risk model based on cholesterol metabolism–Associated gene module for idiopathic pulmonary fibrosis

Background Idiopathic Pulmonary Fibrosis (IPF) is a progressive and fatal lung fibrosis disease with a complex molecular mechanism that remains incompletely understood. Previous studies suggest that metabolic dysregulation, particularly cholesterol metabolism, may play an essential role in the onset and progression of IPF. However, the role of cholesterol metabolism-related genes in IPF and their relationship with prognosis have not been thoroughly explored. This study aims to systematically analyze the potential function of cholesterol metabolism-associated genes in IPF by using bulk transcriptomic data to construct a prognostic risk model and single-cell RNA sequencing data to characterize the cellular context of the model genes. Methods Transcriptomic data (GSE150910 and GSE93606) and single-cell RNA sequencing data from IPF patients were obtained from the GEO database. Differential gene expression analysis was performed using DESeq2 and limma packages to identify significant differentially expressed genes, and a cholesterol metabolism-related gene module was constructed through WGCNA. Subsequently, key cholesterol metabolism genes associated with IPF prognosis were screened using univariate Cox regression and LASSO regression. A prognostic risk model was developed based on the selected biomarkers, and its predictive performance was evaluated using Kaplan-Meier survival curves and ROC curves. To explore the potential biological functions of these genes and their mechanisms in IPF, immune infiltration analysis, GO and KEGG functional enrichment analysis, protein-protein interaction (PPI) network analysis, and GSEA were performed. Finally, mRNA of these genes were validated in animal models. Results A total of 19 differentially expressed cholesterol metabolism-related genes were identified from IPF patients. Through univariate Cox and LASSO regression, three key genes ( PDLIM7 , CFAP45 , and HP ) were selected, and a prognostic risk model for IPF patients was constructed based on these three genes. Kaplan-Meier survival analysis and ROC curves showed that the model demonstrated high predictive performance in both the training and validation cohorts (Area Under the Curve > 0.70). Additionally, immune infiltration analysis revealed significant differences in immune cell infiltration between the high-risk and low-risk groups, suggesting that cholesterol metabolism and immune regulation are closely linked to the progression of IPF. GSEA enrichment analysis highlighted pathways related to lipid metabolism and inflammatory response that were significantly enriched in the high-risk group, and animal experiments showed elevated mRNA expression of these genes in the lungs of fibrotic mice. Conclusion This study constructed a prognostic risk model for IPF using genes derived from a cholesterol metabolism–associated module, and explored their potential roles in IPF progression.