Identifying potential biomarkers of idiopathic pulmonary fibrosis through machine learning analysis

Abstract

Idiopathic pulmonary fibrosis (IPF) is the most common and serious type of idiopathic interstitial pneumonia, characterized by chronic, progressive, and low survival rates, while unknown disease etiology. Until recently, patients with idiopathic pulmonary fibrosis have a poor prognosis, high mortality, and limited treatment options, due to the lack of effective early diagnostic and prognostic tools. Therefore, we aimed to identify biomarkers for idiopathic pulmonary fibrosis based on multiple machine-learning approaches and to evaluate the role of immune infiltration in the disease. The gene expression profile and its corresponding clinical data of idiopathic pulmonary fibrosis patients were downloaded from Gene Expression Omnibus (GEO) database. Next, the differentially expressed genes (DEGs) with the threshold of FDR < 0.05 and |log2 foldchange (FC)| > 0.585 were analyzed via R package "DESeq2" and GO enrichment and KEGG pathways were run in R software. Then, least absolute shrinkage and selection operator (LASSO) logistic regression, support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms were combined to screen the key potential biomarkers of idiopathic pulmonary fibrosis. The diagnostic performance of these biomarkers was evaluated through receiver operating characteristic (ROC) curves. Moreover, the CIBERSORT algorithm was employed to assess the infiltration of immune cells and the relationship between the infiltrating immune cells and the biomarkers. Finally, we sought to understand the potential pathogenic role of the biomarker (SLAIN1) in idiopathic pulmonary fibrosis using a mouse model and cellular model. A total of 3658 differentially expressed genes of idiopathic pulmonary fibrosis were identified, including 2359 upregulated genes and 1299 downregulated genes. FHL2, HPCAL1, RNF182, and SLAIN1 were identified as biomarkers of idiopathic pulmonary fibrosis using LASSO logistic regression, RF, and SVM-RFE algorithms. The ROC curves confirmed the predictive accuracy of these biomarkers both in the training set and test set. Immune cell infiltration analysis suggested that patients with idiopathic pulmonary fibrosis had a higher level of B cells memory, Plasma cells, T cells CD8, T cells follicular helper, T cells regulatory (Tregs), Macrophages M0, and Mast cells resting compared with the control group. Correlation analysis demonstrated that FHL2 was significantly associated with the infiltrating immune cells. qPCR and western blotting analysis suggested that SLAIN1 might be a signature for the diagnosis of idiopathic pulmonary fibrosis. In this study, we identified four potential biomarkers (FHL2, HPCAL1, RNF182, and SLAIN1) and evaluated the potential pathogenic role of SLAIN1 in idiopathic pulmonary fibrosis. These findings may have great significance in guiding the understanding of disease mechanisms and potential therapeutic targets in idiopathic pulmonary fibrosis.

Figure 1

The workflow of this study.

Figure 2

Identification of differentially expressed genes between idiopathic pulmonary fibrosis and normal samples. (A) Volcano plot of the GSE150910 dataset with the cut-off criteria of |log₂FC|> 0.585 and FDR < 0.05. (B) Heatmap visualization of the DEGs between idiopathic pulmonary fibrosis and normal samples.

Figure 3

The results of functional enrichment analyses. (A,B) GO analysis of DEGs. (C,D) KEGG pathway enrichment analysis of DEGs.

Figure 4

Identification of biomarkers of idiopathic pulmonary fibrosis. (A) Characteristic genes selection via LASSO algorithm. (B) Characteristic genes selection via random forest algorithm. (C) Characteristic genes selection via SVM-RFE algorithm. (D) Venn diagram showed the intersection of characteristic genes obtained by the three indicated algorithms. The overlapping characteristic genes represent the biomarkers of idiopathic pulmonary fibrosis.

Figure 5

Box plots of the expression of biomarkers between idiopathic pulmonary fibrosis and normal samples in the training set, including (A) FHL2, (B) HPCAL1, (C) RNF182, and (D) SLAIN1.

Figure 6

Box plots of the expression of biomarkers between idiopathic pulmonary fibrosis and normal samples in the test set, including (A) FHL2, (B) HPCAL1, (C) RNF182, and (D) SLAIN1.

Figure 7

Heatmap of the four biomarkers in the training set (A) and test set (B).

Figure 8

Validation the role of SLAIN1 in vivo and vitro. (A) Photomicrographs of PBS-treated lung sections and Bleomycin-treated lung sections stained with Masson staining and HE staining, respectively. (B) Quantification of mRNA expression levels of SLAIN1 in the mouse model. (C) Western blot for SLAIN1 in the mouse model. Bleomycin-treated group was the experimental group, and PBS-treated group was the control group. (D) Quantification of SLAIN1 expression level in the A549 cells. (E) Western blot of SLAIN1 in A549 cells over time with fibrosis. (F) Quantification of SLAIN1 expression level in the HFL1 cells. (G) Western blot of SLAIN1 in HFL1 cells over time with fibrosis.