Identifying potential biomarkers of idiopathic pulmonary fibrosis through machine learning analysis

作者：Zenan Wu, Huan Chen, Shiwen Ke, Lisha Mo, Mingliang Qiu, Guoshuang Zhu, Wei Zhu, Liangji Liu · 发表于：Scientific Reports · 年份：2023 · DOI：10.1038/s41598-023-43834-z · 被引用次数：45 · 研究领域：Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis、Occupational and environmental lung diseases、Sarcoidosis and Beryllium Toxicity Research

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 cel...