Discovering Biomarkers for Idiopathic Pulmonary Fibrosis by Correlating Serum Extracellular Vesicle Proteins With Clinical Data

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive disease with a poor prognosis due to the irreversible loss of lung structure. Unfortunately, distinguishing between this condition and other interstitial pneumonias (IPs) is quite difficult. Hence, we herein present a machine learning method to identify IPF biomarkers by correlating serum extracellular vesicle proteomic data with clinical data for 507 patients with IPs and 39 healthy control subjects. We applied multi‐class sparse correlation analysis to detecting proteins associated with each disease‐specific IP phenotype, and identified surfactant protein B (SFTPB) and BPI fold containing family B member 1 (BPIFB1) as potential IPF biomarker candidates for discriminating IPF from other IPs, with area under the receiver operating characteristic curve scores of 0.679 and 0.678, respectively, outperforming existing biomarkers for IPF. Combinations of SFTPB and BPIFB1 achieved better discriminative performance over the use of SFTPB or BPIFB1 individually. The identified biomarker candidates can be expected to facilitate the noninvasive diagnosis of IPF.