Yunfei Xiang,1– 3 Guangbin Huang,2 Can Luo,4 Junyu Jiang,1– 3 Tao Zhang,1– 3 Qingbo Zeng,4 Fating Zhou,1,3,5 Dingyuan Du2 

1Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, 40044, People’s Republic of China; 2Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, 40014, People’s Republic of China; 3Chongqing Key Laboratory of Emergency Medicine, Chongqing, 40014, People’s Republic of China; 4Department of Emergency, Affiliated Hospital of Zunyi Medical University Zunyi, Guizhou, 563003, People’s Republic of China; 5Department of Emergency Medicine, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, 40014, People’s Republic of China

Correspondence: Dingyuan Du; Fating Zhou, Chongqing University Central Hospital, No. 1 JianKang Road, Yuzhong District, Chongqing, 40014, People’s Republic of China, Email dudingyuan@qq.com; zhoufating@163.com

Purpose: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive lung disease. PANoptosis, a unique inflammatory programmed cell death, it manifests as the simultaneous activation of signaling markers for pyroptosis, apoptosis, and necroptosis. However, research on the role of PANoptosis in the development of IPF is currently limited. This study was aimed to explore the role of PANoptosis in IPF.
Methods: In this study, we first identified PANDEGs using the GEO database. Exploring potential biological functions and immune cell infiltration abundance through GO/KEGG enrichment analysis and Immune infiltration analysis. Through machine learning and experimental validation, we identified four diagnostic genes and four prognostic genes associated with PANoptosis, leading to the development of a diagnostic and prognostic model for IPF. Our single-cell analysis further explored the role of these PANoptosis prognostic genes. Additionally, the L1000FWD application was used to identify small molecule drugs, based on the four PANoptosis prognostic genes, and confirmed their efficacy through molecular docking.
Results: 104 PANoptosis differentially expressed genes were identified from IPF and normal tissues. Enrichment analysis indicated that these genes were associated with immune-inflammatory response pathway. We developed a diagnostic and prognostic models based on PANoptosis related genes. The diagnostic model included AKT1, PDCD4, PSMA2, and PPP3CB. Conversely, the prognostic model included TNFRSF12A, DAPK2, UACA, and DSP. External dataset validation and qPCR showed the reliability of most of the conclusions. Additionally, potential therapeutic drugs, including Metergoline, Candesartan, and Selumetinib, were identified based on four prognostic genes. Molecular docking shows that these drugs have good binding ability with their targets.
Conclusion: Importantly, our findings provide scientific evidence for the diagnosis and prognostic biomarkers of IPF patients, as well as small molecule therapeutic drugs.

Keywords: biomarkers, IPF, metergoline, candesartan, selumetinib, PANoptosis