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Analysis of immune-related genes in idiopathic pulmonary fibrosis based on bioinformatics and experimental
Xiaoyan Li1, Yuanyuan Huang2, Naixi Ye2
1Clinical Medical College of Chengdu Medical College, Chengdu, China; Department of Endocrinology, the First Affiliated Hospital of Chengdu Medical College, Chengdu, China.
Background:
Idiopathic pulmonary fibrosis (IPF) is a lung disease involving chronic progressive fibrosis, with unclear pathogenesis. In recent years, people have paid increasing attention to the role of immune mechanism. In this study, bioinformatics analysis was used to determine the potential immune-related biomarkers for the diagnosis of IPF, and further analyze the role of immune cell infiltration in the pathogenesis of IPF.
Methods:
The IPF data set (GSE150910) was downloaded from the Gene Expression Omnibus (GEO) database. We used R software to screen differential immune-related genes (IRGs). Least absolute shrinkage and selection operator (LASSO) regression, random forest algorithm, and support vector machine (SVM) were used to screen and determine IPF IRGs to be diagnostic biomarkers. The GSE32537 and GSE10667 data sets were combined into 1 data set to verify the diagnostic efficacy of biomarkers. Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) was used to evaluate the infiltration of immune cells in IPF tissues, and analyze the relationship between diagnostic markers and immune cell infiltration. Meanwhile, clinical specimens were used to verify the diagnostic efficacy of biomarkers and their relationship with immune cell infiltration.
Results:
In this study, 408 participants were involved in the screening to find that PLXNA4 and SLIT2 can be used as diagnostic biomarkers of IPF, and the results were verified by clinical samples. Immune cell infiltration analysis found that regulatory T cells (Tregs), memory B cells, plasma cells, and eosinophils might be involved in the process of IPF. In addition, Tregs were most closely related to PLXNA4 and SLIT2. In clinical samples, forkhead box p3 (FOXP3), a specific marker of Tregs, was positively correlated with PLXNA4 and negatively correlated with SLIT2, which is consistent with the results of bioinformatics analysis.
Conclusions:
The genes PLXNA4 and SLIT2 can be used as diagnostic markers of IPF, and immune cell infiltration plays an important role in the occurrence and development of IPF.
