Xiang Yunfei, Huang Guangbin, Luo Can, Jiang Junyu, Zhang Tao, Zeng Qingbo, Zhou Fating, Du Dingyuan
Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, 40044, People's Republic of China.
Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, 40014, People's Republic of China.
J Inflamm Res. 2024 Dec 24;17:11605-11629. doi: 10.2147/JIR.S490457. eCollection 2024.
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.
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.
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.
Importantly, our findings provide scientific evidence for the diagnosis and prognostic biomarkers of IPF patients, as well as small molecule therapeutic drugs.
特发性肺纤维化（IPF）是一种慢性进行性肺部疾病。PAN细胞焦亡是一种独特的炎症程序性细胞死亡，表现为细胞焦亡、凋亡和坏死性凋亡信号标志物的同时激活。然而，目前关于PAN细胞焦亡在IPF发生发展中的作用的研究有限。本研究旨在探讨PAN细胞焦亡在IPF中的作用。
在本研究中，我们首先使用GEO数据库鉴定PAN差异表达基因（PANDEGs）。通过GO/KEGG富集分析和免疫浸润分析探索潜在的生物学功能和免疫细胞浸润丰度。通过机器学习和实验验证，我们鉴定了与PAN细胞焦亡相关的四个诊断基因和四个预后基因，从而建立了IPF的诊断和预后模型。我们的单细胞分析进一步探讨了这些PAN细胞焦亡预后基因的作用。此外，基于四个PAN细胞焦亡预后基因，使用L1000FWD应用程序鉴定小分子药物，并通过分子对接确认其疗效。
从IPF和正常组织中鉴定出104个PAN细胞焦亡差异表达基因。富集分析表明这些基因与免疫炎症反应途径相关。我们基于PAN细胞焦亡相关基因建立了诊断和预后模型。诊断模型包括AKT1、PDCD4、PSMA2和PPP3CB。相反，预后模型包括TNFRSF12A、DAPK2、UACA和DSP。外部数据集验证和qPCR显示了大多数结论的可靠性。此外，基于四个预后基因鉴定出了潜在的治疗药物，包括麦角新碱、坎地沙坦和司美替尼。分子对接表明这些药物与其靶点具有良好的结合能力。
重要的是，我们的研究结果为IPF患者的诊断和预后生物标志物以及小分子治疗药物提供了科学依据。
