Construction of an E3 Ubiquitin Ligase Gene Model to Predict the Prognosis of Idiopathic Pulmonary Fibrosis Patients Using Integrated Bioinformatics Analysis

Introduction: Idiopathic pulmonary fibrosis (IPF) is a deadly lung disease and currently has limited treatment options. E3 ubiquitin ligases play a role in multiple diseases; however, there are few studies involving them in the development of IPF. This study aimed to develop an E3 ubiquitin ligase gene-based risk signature model to predict the prognosis of patients with IPF. Methods: We downloaded the GSE70866 dataset and the E3 ubiquitin ligase genes from the GEO database and the iUUCD database, respectively. We then used LASSO and multivariate Cox regression analysis to develop a prognostic signature model and validated its efficacy using the GEO dataset. Functional enrichment analysis, immune cell infiltration, and consensus clustering analysis were performed based on the model. Transcription factors associated with the genes in the model were identified using the hTFtarget database. scRNA-seq analysis identified key cell types through the analyses of cell communication and differentiation trajectories. The expression of the E3 genes in the model was detected by Western blot. Results: A prognostic model based on 5 E3 ubiquitin ligase genes (CDCA3, TRIM47, SH3RF1, SPAG16, LONRF3) was developed. The high expression of CDCA3, TRIM47, and SH3RF1 predicts the poor prognosis of IPF patients. Functional enrichment analysis indicated the functional difference between high- and low-risk groups. And the model is enriched in the signaling pathway related to fibrosis. Immune cell infiltration analysis revealed 22 immune cell types related to the model differed significantly between the two risk groups. Single-cell RNA analysis revealed that alveolar epithelial cells (AEC) have a strong interaction with macrophages, based on the model and the potential role of the 5 E3 ligase genes in IPF. Finally, the western blot results demonstrated that CDCA3, SH3RF1, and TRIM47 were expressed at higher levels in the model with IPF compared to normal, while SPAG16 was expressed at lower levels in IPF. Discussion: In this study, the prognostic model constructed using the data from the GEO database significantly improved the accuracy of individualized prognosis prediction in patients with pulmonary fibrosis. The high-risk populations identified by this model may benefit from early intervention, providing an objective tool for informed clinical decision-making. In addition, the strong predictive signatures in the model suggest its potential pathological mechanism value, which points out the direction for future targeted research. Conclusion: Our study highlights the E3 ubiquitin ligase gene-based risk model as a promising tool for enhancing prognostic accuracy in IPF.