Detection of microRNA expression levels based on microarray analysis for classification of idiopathic pulmonary fibrosis

The etiology and pathophysiological mechanisms of idiopathic pulmonary fibrosis (IPF) are yet to be fully elucidated; however, mining of diseaserelated microRNAs (miRNAs/miRs) has improved the understanding of the progression of IPF. The aim of the current study was to screen miRNAs associated with IPF using three mathematical algorithms: Oneway ANOVA, least absolute shrinkage and selector operation (LASSO) and support vector machine­recursive feature elimination (SVMRFE). Using ANOVA, three miRNAs and two miRNAs were selected with opposite expression patterns in moderate and severe IPF, respectively. In total, two algorithms, LASSO and SVMRFE, were used to perform feature selection of miRNAs. miRNAs from patients were also extracted from formalinfixed paraffinembedded tissues and detected using reverse transcriptionquantitative PCR (RTqPCR). The intersection of the three algorithms (ANOVA, LASSO and SVMRFE) was taken as the final result of the miRNA candidates. Three miRNA candidates, including miR124, hsamiR5245p and hsamiR194 were therefore used as biomarkers. The receiver operating characteristic model demonstrated favorable discrimination between IPF and control groups, with an area under the curve of 78.5%. Moreover, RTqPCR results indicated that miR124, hsamiR5245p, hsamiR194 and hsamiR133a were differentially expressed between patients with IPF and agematched men without fibrotic lung disease. The target genes of these miRNAs were further predicted and Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed. Collectively, the present results suggested that the identified miRNAs associated with IPF may be useful biomarkers for the diagnosis of this disease.