Dongguang Wang,1,2,* Yifan Yuan,3– 5,* Xiang Tong,1,2 Lian Wang,1,2 Jibo Sun,1,2 Shijie Zhang,1,2 Sitong Liu,1,2 Huatian Gan,3– 5 Hong Fan1,2 

1Department of Respiratory and Critical Care Medicine, and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, People’s Republic of China; 2State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, People’s Republic of China; 3Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, People’s Republic of China; 4Centre for Inflammatory Bowel Disease, West China Hospital, Sichuan University, Chengdu, People’s Republic of China; 5Lab of Inflammatory Bowel Disease, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Huatian Gan, Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, People’s Republic of China, Email ganhuatian123@163.com Hong Fan, Department of Respiratory and Critical Care Medicine, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, People’s Republic of China, Email fanhong@scu.edu.cn

Aim: To identify the molecular signature of differentially expressed genes (DEGs) associated with PANoptosis in idiopathic pulmonary fibrosis (IPF) and to interpret their immune landscape and cellular distribution characteristics.
Methods and Results: We acquired two IPF datasets from the Gene Expression Omnibus (GEO) database to identify PANoptosis-related DGEs (PAN-DEGs), initially identifying thirty PAN-DEGs. Utilizing machine learning algorithms, we established a five-gene PANoptosis-related signature comprising IGF1, GPX3, GADD45β, SMAD7, and TIMP3, each demonstrating robust diagnostic performance. The expression of these hub genes was subsequently validated using a third GEO dataset and a bleomycin-induced pulmonary fibrosis model. Immune infiltration analysis revealed a close association of these genes with various immune cells, and single-cell RNA sequencing indicated significant expression changes in diverse pulmonary cell types, particularly endothelial cells and fibroblasts.
Conclusion: We identified and validated a PANoptosis-related gene signature in IPF, providing insights into their immune infiltration and potential cellular distribution. Further research is necessary to elucidate the biological functions and mechanisms of these genes in the pathogenesis of IPF.

Keywords: bioinformatics analysis, differentially expressed genes, idiopathic pulmonary fibrosis, PANoptosis