Predicting biomarkers related to idiopathic pulmonary fibrosis: Robust ranking aggregation analysis and animal experiment verification.

Abstract

Idiopathic pulmonary fibrosis (IPF) is a progressive and incurable lung disease characterized by unknown etiology. This study employs robust ranking aggregation to identify consistent differential genes across multiple datasets, aiming to enhance prognostic evaluation and facilitate the development of more effective immunotherapy strategies for IPF. Using the GSE10667, GSE110147, and GSE24206 datasets, the analysis identifies 92 robust differentially expressed genes (DEGs), including SPP1, IGF1, ASPN, and KLHL13, highlighted as potential biomarkers through machine learning and experimental validation. Additionally, significant differences in immune cell types between IPF samples and controls, such as Plasma cells, Macrophages M0, Mast cells resting, T cells CD8, and NK cells resting, inform the construction of diagnostic and survival prediction models, demonstrating good applicability. These findings provide insights into IPF pathophysiology and suggest potential therapeutic targets.