Identification and immunological characterization of cuproptosis-related molecular clusters in idiopathic pulmonary fibrosis disease - Shi, Xuefeng; Pan, Zhilei; Cai, Weixiu; Zhang, Yuhao; Duo, Jie; Liu, Ruitian; Cai, Ting (Frontiers in Immunology, 2023)
Background: Idiopathic pulmonary fibrosis (IPF) has attracted considerable attention worldwide and is challenging to diagnose. Cuproptosis is a new form of cell death that seems to be associated with various diseases. However, whether cuproptosis-related genes (CRGs) play a role in regulating IPF disease is unknown. This study aims to analyze the effect of CRGs on the progression of IPF and identify possible biomarkers. Methods: Based on the GSE38958 dataset, we systematically evaluated the differentially expressed CRGs and immune characteristics of IPF disease. We than explored the cuproptosis-related molecular clusters, the related immune cell infiltration, and the biological characteristics analysis. Subsequently, a weighted gene co-expression network analysis (WGCNA) was performed to identify cluster-specific differentially expressed genes. Lastly, the XGB machine-learning model was chosen for the analysis of prediction and external datasets validated the predictive efficiency. Results: Nine differentially expressed CRGs were identified between healthy and IPF patients. IPF patients showed higher monocytes and monophages M0 infiltration and lower naive B cells and memory resting T CD4 cells infiltration than healthy individuals. A positive relationship was found between activated dendritic cells and CRGs of LIPT1, LIAS, GLS, and DBT. We also identified cuproptosis subtypes in IPF patients. Go and KEGG pathways analysis demonstrated that cluster-specific differentially expressed genes in Cluster 2 were closely related to monocyte aggregation, ubiquitin ligase complex, and ubiquitin-mediated proteolysis, among others. We also constructed an XGB machine model to diagnose IPF, presenting the best performance with a relatively lower residual and higher area under the curve (AUC= 0.700) and validated by external validation datasets (GSE33566, AUC = 0.700). The analysis of the nomogram model demonstrated that XKR6, MLLT3, CD40LG, and HK3 might be used to diagnose IPF disease. Further analysis revealed that CD40LG was significantly associated with IPF. Conclusion: Our study systematically illustrated the complicated relationship between cuproptosis and IPF disease, and constructed an effective model for the diagnosis of IPF disease patients.