Elucidating the pathway activity and prognostic significance of diverse cell-death patterns in idiopathic pulmonary fibrosis

Background Idiopathic pulmonary fibrosis (IPF) is one of the interstitial lung diseases (ILDs) with poor prognosis. Multiple regulated cell death (RCD) pathways are involved in regulating the progression of pulmonary fibrosis at different stages. Methods A total of 20 RCD pathways and crucial regulatory genes were collected from available literature. The study initially elucidated the profiling of 20 kinds of RCD pathways in normal and fibrotic lung tissues based on the scRNAseq dataset and bulk-RNAseq dataset. Targets associated with IPF were identified by Mendelian randomization analysis, and univariate Cox regression was used to further identify RCD-related genes significantly associated with overall survival (OS). A combination of 101 distinct machine-learning algorithms was utilized to develop a prognostic signature. In addition, we investigated the relationship between prognostic signature and clinical characteristics. Results By integrating scRNAseq data and bulk-RNAseq data, the study initially elucidated the pathway activity associated with distinct RCD patterns in IPF patients. In addition, following detailed research of various RCD patterns, the study developed the CDI signature with 13 genes, which combined with multiple machine learning methods to generate CDI signature has a strong predictive influence on the prognosis of IPF patients. As proven by independent datasets, IPF patients with high CDI had a poorer outcome. From the clinical characteristics, IPF patients with high CDI have impaired lung function. Finally, a nomogram with strong predictive ability was generated by integrating CDI with clinical characteristics. Conclusion In summary, we have developed a novel CDI model that effectively forecasts the clinical prognosis of patients with IPF by integrating various cell death patterns.