An 8-ferroptosis-related genes signature from Bronchoalveolar Lavage Fluid for prognosis in patients with idiopathic pulmonary fibrosis

Background With the rapid advances of genetic and genomic technologies, the pathophysiological mechanisms of idiopathic pulmonary fibrosis (IPF) were gradually becoming clear, however, the prognosis of IPF was still poor. This study aimed to systematically explore the ferroptosis-related genes model associated with prognosis in IPF patients. Methods Datasets were collected from the Gene Expression Omnibus (GEO). The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to create a multi-gene predicted model from patients with IPF in the Freiburg cohort of the GSE70866 dataset. The Siena cohort and the Leuven cohort were used for validation. Results Nineteen differentially expressed genes (DEGs) between the patients with IPF and control were associated with poor prognosis based on the univariate Cox regression analysis (all P < 0.05). According to the median value of the risk score derived from an 8-ferroptosis-related genes signature, the three cohorts' patients were stratified into two risk groups. Prognosis of high-risk group (high risk score) was significantly poorer compared with low-risk group in the three cohorts. According to multivariate Cox regression analyses, the risk score was an independently predictor for poor prognosis in the three cohorts. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) confirmed the signature's predictive value in the three cohorts. According to functional analysis, inflammation- and immune-related pathways and biological process could participate in the progression of IPF. Conclusions These results imply that the 8-ferroptosis-related genes signature in the bronchoalveolar lavage samples might be an effective model to predict the poor prognosis of IPF.

Automated summary

By analyzing gene expression datasets, this study identified 19 differentially expressed genes associated with prognosis in IPF patients and created a multi-gene predictive model. The model stratified patients into high-risk and low-risk groups based on a risk score, with significantly poorer prognosis in the high-risk group. Functional analysis also revealed the involvement of inflammation- and immune-related pathways in the progression of IPF.