Ferroptosis-Related Genes in Bronchoalveolar Lavage Fluid Serves as Prognostic Biomarkers for Idiopathic Pulmonary Fibrosis

Abstract

Background: Idiopathic pulmonary fibrosis (IPF) is a chronic progressive disease with unknown etiology and unfavorable prognosis. Ferroptosis is a form of regulated cell death with an iron-dependent way that is involved in the development of various diseases. Whereas the prognostic value of ferroptosis-related genes (FRGs) in IPF remains uncertain and needs to be further elucidated. Methods: The FerrDb database and the previous studies were screened to explore the FRGs. The data of patients with IPF were obtained from the GSE70866 dataset. Wilcoxon's test and univariate Cox regression analysis were applied to identify the FRGs that are differentially expressed between normal and patients with IPF and associated with prognosis. Next, a multigene signature was constructed by the least absolute shrinkage and selection operator (LASSO)-penalized Cox model in the training cohort and evaluated by using calibration and receiver operating characteristic (ROC) curves. Then, 30% of the dataset samples were randomly selected for internal validation. Finally, the potential function and pathways that might be affected by the risk score-related differently expressed genes (DEGs) were further explored. Results: A total of 183 FRGs were identified by the FerrDb database and the previous studies, and 19 of them were differentially expressed in bronchoalveolar lavage fluid (BALF) between IPF and healthy controls and associated with prognosis (p < 0.05). There were five FRGs (aconitase 1 [ACO1], neuroblastoma RAS viral (v-ras) oncogene homolog [NRAS], Ectonucleotide pyrophosphatase/phosphodiesterase 2 [ENPP2], Mucin 1 [MUC1], and ZFP36 ring finger protein [ZFP36]) identified as risk signatures and stratified patients with IPF into the two risk groups. The overall survival rate in patients with high risk was significantly lower than that in patients with low risk (p < 0.001). The calibration and ROC curve analysis confirmed the predictive capacity of this signature, and the results were further verified in the validation group. Risk score-related DEGs were found enriched in ECM-receptor interaction and focal adhesion pathways. Conclusion: The five FRGs in BALF can be used for prognostic prediction in IPF, which may contribute to improving the management strategies of IPF.

Keywords: ferroptosis; idiopathic pulmonary fibrosis; model; prognostic; signature.

Figure 1

Flow diagram of the present study.

Figure 2

Identification of the FRGs in the patients with IPF from the Gene Expression Omnibus (GEO) database. (A) The expression of the 79 ferroptosis-related DEGs in IPF. (B) The 45 ferroptosis-related prognostic genes in IPF. p < 0.05 was considered significant. FRGs, ferroptosis-related genes; IPF, idiopathic pulmonary fibrosis; and DEGs, differentially expressed genes.

Figure 3

Identification and expression of the overlapping genes between DEGs and prognostic genes in IPF. (A) Venn diagram of 19 overlapping genes. (B) Expression of the 19 overlapping genes in IPF. (C) The correlation network of the overlapping genes. IPF, idiopathic pulmonary fibrosis; DEGs, differentially expressed genes; PPI, protein–protein interaction.

Figure 4

The construction of the prognostic model of IPF in the training group. (A) LASSO coefficients profiles of the 19 overlapping genes. (B) LASSO regression with 10-fold cross-validation obtained eight prognostic FRGs. (C) The multivariate Cox regression analysis identified five prognostic FRGs for prognostic model construction. (D) The nomogram of the prognostic model based on the five FRGs. The calibration curves of the nomogram for predicting 3- (E) and 5-years survival (F) of the patients with IPF. IPF, idiopathic pulmonary fibrosis; FRGs, ferroptosis-related genes. *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 5

Prognostic analysis of the five-FRGs signature in the training group. Distribution of IPF samples (A) and OS status (B) based on the median risk score. PCA (C) and t-SNE (D) analysis of the training group. Kaplan–Meier survival (E) and time-dependent ROC curve (F) analysis of the five-FRGs signature in training group. (G) Heatmap of the expression profiles of the five-FRGs in low- and high-risk groups. FRGs, ferroptosis-related genes; IPF, idiopathic pulmonary fibrosis; OS, overall survival; PCA, principal component analysis; t-SNE, t-distributed stochastic neighbor embedding; ROC, receiver operating characteristic.

Figure 6

Internal validation of the five-FRGs signature. Distribution of IPF samples (A) and OS status (B) based on the median value of the risk scores. PCA (C) and t-SNE (D) analysis of the testing group. Kaplan–Meier survival (E) and time-dependent ROC curve (F) analysis of the five-FRGs signature in the testing group. (G) Heatmap of the expression profiles of the five-FRGs in low- and high-risk groups of the testing cohort. FRGs, ferroptosis-related genes; IPF, idiopathic pulmonary fibrosis; OS, overall survival; PCA, principal component analysis; t-SNE, t-distributed stochastic neighbor embedding; ROC, receiver operating characteristic.

Figure 7

Independent prognostic analysis of clinical parameters and risk score. The univariate (A) and multivariate (B) Cox regression analysis of the associations between the risk score, the clinical parameters and OS of IPF patients in the training cohort. The univariate (C) and multivariate (D) Cox regression analysis of the associations between the risk score, clinical parameters and OS of IPF patients in the testing cohort. IPF, idiopathic pulmonary fibrosis; OS, overall survival.

Figure 8

GO and KEGG analysis. The GO enrichment and KEGG pathways of the risk score-related DEGs in the training cohort (A,B) and testing cohort (C,D). GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genome; DEGs, differentially expressed genes.