BACKGROUND: Studies suggest that immune and inflammation processes may be involved in the development of idiopathic pulmonary fibrosis (IPF)
however, their roles remain unclear. This study aims to identify key genes associated with immune response and inflammation in IPF using bioinformatics. METHODS: We identified differentially expressed genes (DEGs) in the GSE93606 dataset and GSE28042 dataset, then obtained differentially expressed immune- and inflammation-related genes (DE-IFRGs) by overlapping DEGs. Two machine learning algorithms were used to further screen key genes. Genes with an area under curve (AUC) of >
0.7 in receiver operating characteristic (ROC) curves, significant expression and consistent trends across datasets were considered key genes. Based on these key genes, we carried out nomogram construction, enrichment and immune analyses, regulatory network mapping, drug prediction, and expression verification. RESULTS: 27 DE-IFRGs were identified by intersecting 256 DEGs, 1793 immune-related genes, and 1019 inflammation-related genes. Three genes ( CONCLUSION: This study identified three key genes (