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<p>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, results from qRT-PCR showed a significant increase in the expression of MLKL and AIM2 in the lung tissue of the IPF animal model.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月10日

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  • Zhihua Wang,
  • Quanlei Li,
  • Yuntian Chen,
  • Lixing Gan,
  • Lifen Yuan,
  • Juan Liu,
  • Yu Xie,
  • Tianyu Zhou,
  • Xiahui Ge

摘要

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, results from qRT-PCR showed a significant increase in the expression of MLKL and AIM2 in the lung tissue of the IPF animal model.