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 disease-related 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: One-way ANOVA, least absolute shrinkage and selector operation (LASSO) and support vector machine-recursive feature elimination (SVM-RFE). 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 SVM-RFE, were used to perform feature selection of miRNAs. miRNAs from patients were also extracted from formalin-fixed paraffin-embedded tissues and detected using reverse transcription-quantitative PCR (RT-qPCR). The intersection of the three algorithms (ANOVA, LASSO and SVM-RFE) was taken as the final result of the miRNA candidates. Three miRNA candidates, including miR-124, hsa-miR-524-5p and hsa-miR-194 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, RT-qPCR results indicated that miR-124, hsa-miR-524-5p, hsa-miR-194 and hsa-miR-133a were differentially expressed between patients with IPF and age-matched 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.