Machine learning potential predictor of idiopathic pulmonary fibrosis

TL;DR

This study identifies PODNL1 and PIGA as potential biomarkers for idiopathic pulmonary fibrosis, which could improve early risk prediction and understanding of the disease.

Contribution

The study introduces PODNL1 and PIGA as novel biomarkers for idiopathic pulmonary fibrosis, validated through machine learning and experimental methods.

Findings

PODNL1 and PIGA were identified as potential biomarkers for IPF onset.

The biomarkers showed predictive accuracy confirmed by ROC curve analysis.

Immune cell infiltration was found to correlate significantly with IPF onset.

Abstract

Idiopathic pulmonary fibrosis (IPF) is a severe chronic respiratory disease characterized by treatment challenges and poor prognosis. Identifying relevant biomarkers for effective early-stage risk prediction is therefore of critical importance.

In this study, we obtained gene expression profiles and corresponding clinical data of IPF patients from the GEO database. GO enrichment and KEGG pathway analyses were performed using R software. To construct an IPF risk prediction model, we employed LASSO-Cox regression analysis and the SVM-RFE algorithm. PODNL1 and PIGA were identified as potential biomarkers associated with IPF onset, and their predictive accuracy was confirmed using ROC curve analysis in the test set. Furthermore, GSEA revealed enrichment in multiple pathways, while immune function analysis demonstrated a significant correlation between IPF onset and immune cell infiltration. Finally, the roles of PODNL1 and PIGA as biomarkers were validated through in vivo and in vitro experiments using qRT-PCR, Western blotting, and immunohistochemistry.

These findings suggest that PODNL1 and PIGA may serve as critical biomarkers for IPF onset and contribute to its pathogenesis.

This study highlights their potential for early biomarker discovery and risk prediction in IPF, offering insights into disease mechanisms and diagnostic strategies.

Figures