Exploring the role of PANoptosis in idiopathic pulmonary fibrosis based on scRNA-seq and bulk-seq

作者：Zhihua Wang, Quanlei Li, Yahong Chen, Li Gan, Lifen Yuan, Juan Liu, YuLong Xie, Tianyu Zhou, Xiahui Ge · 发表于：Clinical and Experimental Medicine · 年份：2025 · DOI：10.1007/s10238-025-01990-8 · 被引用次数：1 · 研究领域：Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis、Ferroptosis and cancer prognosis、Inflammasome and immune disorders

PANoptosis is a novel form of programmed cell death that integrates pyroptosis, apoptosis, and necroptosis; in this study, we combined the single-cell RNA sequencing (scRNA-seq) dataset GSE227136 with transcriptome data to elucidate its role in idiopathic pulmonary fibrosis (IPF) pathogenesis. PANoptosis-related genes were compiled from GeneCards and published literature. Consensus clustering was used to identify distinct PANoptosis-related clusters of IPF in the GEO dataset based on filtered PANoptosis-related differentially expressed genes (PRDEGs). Specific hub genes were identified using weighted gene co-expression network analysis (WGCNA) and two machine learning methodologies, which were used to develop predictive models. The inflammatory programmed cell death score (PANoptosis score, Ps) for each IPF patient was calculated based on nine PRDEGs, followed by analyses of these PRDEGs' expression differences and their ROC curves. PRDEG expression was confirmed in murine pulmonary tissues using quantitative real-time polymerase chain reaction (qRT-PCR). We successfully identified nine PRDEGs and two distinct PANoptosis-related clusters with these PRDEGs. Using WGCNA and machine learning approaches, we constructed a nomogram with robust predictive capacity for diagnosis of IPF. In addition, immune infiltration analysis among different molecular groups and single cell analysis revealed that increased PANoptosis activity was closely associated with immune activation. Finally, ...