Machine learning identified MDK score has prognostic value for idiopathic pulmonary fibrosis based on integrated bulk and single cell expression data

Abstract

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease that poses a significant challenge to medical professionals due to its increasing incidence and prevalence coupled with the limited understanding of its underlying molecular mechanisms. In this study, we employed a novel approach by integrating five expression datasets from bulk tissue with single-cell datasets; they underwent pseudotime trajectory analysis, switch gene selection, and cell communication analysis. Utilizing the prognostic information derived from the GSE47460 dataset, we identified 22 differentially expressed switch genes that were correlated with clinical indicators as important genes. Among these genes, we found that the midkine (MDK) gene has the potential to serve as a marker of Idiopathic pulmonary fibrosis because its cellular communicating genes are differentially expressed in the epithelial cells. We then utilized midkine and its cellular communication-related genes to calculate the midkine score. Machine learning models were further constructed through midkine and related genes to predict Idiopathic pulmonary fibrosis disease through the bulk gene expression datasets. The midkine score demonstrated a correlation with clinical indexes, and the machine learning model achieved an AUC of 0.94 and 0.86 in the Idiopathic pulmonary fibrosis classification task based on lung tissue samples and peripheral blood mononuclear cell samples, respectively. Our findings offer valuable insights into the pathogenesis of Idiopathic pulmonary fibrosis, providing new therapeutic directions and target genes for further investigation.