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Publications

Journal Article Epublish

PubMed: 39959639 DOI: 10.2147/JIR.S489210

Identification and Analysis of Key Immune- and Inflammation-Related Genes in Idiopathic Pulmonary Fibrosis.

J Inflamm Res 18:1993-2009

Authors: Yan Tan, Baojiang Qian, Qiurui Ma, Kun Xiang, Shenglan Wang

Affiliations

  • Department of Respiratory and Critical Care Medicine, the First People's Hospital of Yunnan Province, Kunming, People's Republic of China.
  • Medical School of Kunming University of Science and Technolog, Kunming, People's Republic of China.

Abstract

Background

Studies suggest that immune and inflammation processes may be involved in the development of idiopathic pulmonary fibrosis (IPF); however, their roles remain unclear. This study aims to identify key genes associated with immune response and inflammation in IPF using bioinformatics.

Methods

We identified differentially expressed genes (DEGs) in the GSE93606 dataset and GSE28042 dataset, then obtained differentially expressed immune- and inflammation-related genes (DE-IFRGs) by overlapping DEGs. Two machine learning algorithms were used to further screen key genes. Genes with an area under curve (AUC) of > 0.7 in receiver operating characteristic (ROC) curves, significant expression and consistent trends across datasets were considered key genes. Based on these key genes, we carried out nomogram construction, enrichment and immune analyses, regulatory network mapping, drug prediction, and expression verification.

Results

27 DE-IFRGs were identified by intersecting 256 DEGs, 1793 immune-related genes, and 1019 inflammation-related genes. Three genes () were obtained by crossing two machine algorithms (Boruta and LASSO),which had good diagnostic performance with AUC values. These key genes were all enriched in the same pathways, such as GOCC_azurophil_granule, IL-12 signalling and production in macrophages is the pathway with the strongest role for key genes. Six distinct immune cells, including naive CD4 T cells, T cells CD4 memory resting, T cells regulatory (Tregs), Monocytes, Macrophages M2, Neutrophils were identified. Real-time quantitative polymerase chain reaction (RT-qPCR) results were consistent with the training and validation sets, and the expression of these key genes was significantly upregulated in the IPF samples.

Conclusion

This study identified three key genes ( and ) associated with immune response and inflammation in IPF, providing valuable insights into the diagnosis and treatment of IPF.

MeSH

Cells, Genes, Idiopathic Pulmonary Fibrosis, Area Under Curve, Fibrosis, Algorithms, Machine Learning, Pulmonary Fibrosis, Immunity