A novel seven-gene risk profile in BALF to identify high-risk patients with idiopathic pulmonary fibrosis

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a fatal heterogeneous disease with a varied clinical course that is difficult to predict. Accurate predictive models are urgently needed to identify individuals with poor survival for the optimal timing of referral for transplantation and provide some clues for mechanistic research on disease progression. METHODS: We obtained the gene expression profiles of bronchoalveolar lavage fluid (BALF) from the Gene Expression Omnibus. Individuals from the GPL14550 platform were assigned to the derivation cohort (n=112) and individuals from the GPL17077 platform to the validation cohort (n=64). Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were applied to select candidate genes for overall survival. A nomogram model was constructed based on Cox hazard regression analysis. The model was assessed by C-statistic, calibration curve, and decision curve analysis (DCA) and was externally validated. RESULTS: A nomogram model comprising seven genes was constructed. Excellent discrimination and calibration were observed in the derivation (C-index 0.815) and validation (C-index 0.812) cohorts. The AUCs for predicting 1-, 2- and 3-year survival were 0.857, 0.918, 0.930 in the derivation cohort and 0.850, 0.880, 0.925 in the validation cohort, respectively. DCA confirmed the clinical applicability of the model. A risk score based on the model was an independent prognostic predictor and could divide patients into high- and low-risk groups. The Kaplan-Meier analysis displayed that high-risk patients exhibited significantly poorer survival compared with low-risk patients. Gene Set Enrichment Analysis (GSEA) showed that high-risk patients were primarily enriched in inflammatory hallmarks, and single sample GSEA (ssGSEA) indicated that the high-risk group is closely correlated with the immune process. These lead to increased insight into mechanisms associated with IPF progression that inflammation mediated by immune response might be involved in the disease progression. CONCLUSIONS: The novel BALF seven-gene model performed well in risk stratification and individualized survival prediction for patients with IPF, facilitating personalized management of IPF patients. It deepened the understanding of the role of inflammation in IPF progression, which needs to be further studied.