Integrated bioinformatics analysis screened the key genes and pathways of idiopathic pulmonary fibrosis

To investigate the molecular mechanisms underlying idiopathic pulmonary fibrosis (IPF), we analyzed the GSE173355 and GSE173356 datasets obtained from the NCBI-GEO database. We identified differentially expressed genes (DEGs) and differentially methylated sites. Functional enrichment analysis was conducted for both DEGs and differentially methylated sites. Functional enrichment analysis was performed for both DEGs and differentially methylated sites, alongside an examination of immune-related scores, proportions, and GSVA enrichment scores of immune cells in IPF versus control samples. An integrated gene-methylation association analysis revealed 8 genes with expression levels negatively influenced by methylation. The Rap1 pathway, Focal adhesion, and Axon guidance were significantly enriched among both DEGs and differentially methylated sites. Immune-related scores were notably lower in the IPF group compared to the control group, with marked differences in immune cell proportions and GSVA enrichment scores. Screening of DEGs identified 361 differentially expressed immune-related genes (IRGs). Protein-protein interaction (PPI) network analysis using the STRING database and Cytoscape software unveiled 10 key genes and 3 core subnetworks. RT-qPCR validation in bleomycin-induced IPF mouse model and A549 EMT model confirmed the reliability of most findings. These results provide new insights into IPF pathogenesis and potential therapeutic strategies, necessitating further functional validation.

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Publication Details

Journal
Scientific Reports
Published
2025-04-25
DOI
https://doi.org/10.1038/s41598-025-97037-9
Primary Topic
Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
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article
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article

Integrated bioinformatics analysis screened the key genes and pathways of idiopathic pulmonary fibrosis

Yangyang Wei, Ting Xue, Yu X, Kang Hong et al.
Scientific Reports
Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
article

Integrated bioinformatics analysis screened the key genes and pathways of idiopathic pulmonary fibrosis

Yangyang Wei, Ting Xue, Yu X, Kang Hong, Xiaomei Kong, Juan Wu, Shuting Wei
article en

Abstract

To investigate the molecular mechanisms underlying idiopathic pulmonary fibrosis (IPF), we analyzed the GSE173355 and GSE173356 datasets obtained from the NCBI-GEO database. We identified differentially expressed genes (DEGs) and differentially methylated sites. Functional enrichment analysis was conducted for both DEGs and differentially methylated sites. Functional enrichment analysis was performed for both DEGs and differentially methylated sites, alongside an examination of immune-related scores, proportions, and GSVA enrichment scores of immune cells in IPF versus control samples. An integrated gene-methylation association analysis revealed 8 genes with expression levels negatively influenced by methylation. The Rap1 pathway, Focal adhesion, and Axon guidance were significantly enriched among both DEGs and differentially methylated sites. Immune-related scores were notably lower in the IPF group compared to the control group, with marked differences in immune cell proportions and GSVA enrichment scores. Screening of DEGs identified 361 differentially expressed immune-related genes (IRGs). Protein-protein interaction (PPI) network analysis using the STRING database and Cytoscape software unveiled 10 key genes and 3 core subnetworks. RT-qPCR validation in bleomycin-induced IPF mouse model and A549 EMT model confirmed the reliability of most findings. These results provide new insights into IPF pathogenesis and potential therapeutic strategies, necessitating further functional validation.

Scientific ReportsVol. 15(1)
Shanxi Medical University (CN), First Hospital of Shanxi Medical University (CN)
Zero hunger
Openalex Percentile: Top 86%
Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
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Integrated bioinformatics analysis screened the key genes and pathways of idiopathic pulmonary fibrosis — Yangyang Wei, Ting Xue, et al. · Scientific Reports (2025) | TGRS Research Map | TGRS