Mechanistic analysis of Th2-type inflammatory factors in asthma

Abstract

Background: The main pathological features of asthma are widespread chronic inflammation of the airways and restricted ventilation due to airway remodeling, which involves changes in a range of regulatory pathways. While the role of T helper type 2 (Th2)-related inflammatory factors in this process is known, the detailed understanding of how genes affect protein functions during airway remodeling is still lacking. This study aims to fill this knowledge gap by integrating gene expression data and protein function analysis, providing new scientific insights for a deeper understanding of the mechanisms of airway remodeling and for further development of asthma treatment strategies.

Methods: In this study, the mechanism of Th2-related inflammatory factors in tracheal remodeling was studied through differentially expressed gene (DEG) screening, enrichment analysis, protein-protein interaction (PPI) network construction, machine learning, and the construction of a line graph model.

Results: Our study revealed that S100A14, KRT6A, S100A2, ABCA13, UBE2C, RASSF10, PSCA, PLAT, and TIMP1 may be the key genes for airway remodeling; epithelial-mesenchymal transition (EMT)-related genes GEM, TPM4, SLC6A8, and SNTB1 may be involved in airway remodeling due to asthma; IL6 may affect the occurrence of airway remodeling by binding to UBE2C protein or by regulating GEM genes, respectively; IL6 and IL9 may affect the occurrence of airway remodeling by regulating the downstream Toll-like receptor (TLR) signaling pathway and thus IL6 and IL9 may influence the occurrence of tracheal remodeling by regulating downstream TLR signaling pathways.

Conclusions: This study further mined the asthma gene microarray database through bioinformatics analysis and identified key genes and important pathways affecting airway remodeling in asthma patients, providing new ideas to uncover the mechanism of airway remodeling due to asthma and then seek new therapeutic targets.

Keywords: T helper type 2-associated inflammatory factors (Th2-associated inflammatory factors); airway inflammation; airway remodeling; asthma.

Figure 1

Identification and enrichment analysis of DEGs. (A) Volcano map of DEGs in the GSE63142 dataset. Different colors represent multiples of gene differences, with red indicating increased gene expression (the redder the higher) and green indicating decreased gene expression (the greener the lower). (B) BP. (C) CC. (D) MF. (E) KEGG. FC, fold change; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes.

Figure 2

Screening of tracheal remodeling-related genes. (A) Volcano map of DEGs in the GSE109365 data set. Red represents an increase in gene expression [logFC >1.5], and green represents a decrease in gene expression [logFC <−1.5]. (B) The intersection of DEGs in GSE63142 and GSE109365 data sets was taken. (C) Heat map analysis of the expression of 11 genes in the GSE63142 dataset. (D) Histogram analysis of the expression of 11 genes in the GSE63142 dataset. **, P<0.01; ***, P<0.001. FC, fold change; DEGs, differentially expressed genes.

Figure 3

RF algorithm for screening tracheal remodeling differential trait genes. (A,B) Comparison of the accuracy of RF algorithm and SVM algorithm for 11 tracheal remodeling-related genes. (C) Comparison of the specificity and sensitivity of RF algorithm and SVM algorithm for 11 tracheal remodeling-related genes. (D,E) RF method was used to screen 11 signature genes (S100A14, KRT6A, S100A2, ABCA13, UBE2C, RASSF10, PSCA, PLAT, TIMP1, GSTA3 and TFF1). RF, random forest; SVM, support vector machine.

Figure 4

Construction of nomogram prediction model for the risk of trachea remodeling caused by asthma. (A) Nomogram predicting the risk of tracheal remodeling. (B) ROC curve and calibration curve analysis of nomogram model. (C) DCA curve analysis of nomogram model. (D) Clinical impact curve analysis of nomogram model. ROC, receiver operating characteristic; DCA, decision curve analysis.

Figure 5

Correlation analysis of Th2-type airway inflammatory factors and key genes of tracheal remodeling. (A) Differential expression of ten Th2-type airway inflammatory factors in the GSE63142 dataset. (B) Correlation analysis of IL6 with nine airway remodeling signature genes. (C) Correlation analysis of IL9 with nine airway remodeling signature genes. (D) PPI network construction. (E) Molecular docking of IL6 and UBE2C. (F) Molecular docking of IL6 with TIMP1. *, P<0.05; ***, P<0.001. abs (cor), correlation coefficient; Th2, T helper type 2; PPI, protein-protein interaction.

Figure 6

Identification and analysis of EMT-related DEGs. (A) Heat map analysis of EMT-related DEGs in GSE63142 dataset. (B) Comparison of the accuracy of RF algorithm and SVM algorithm, the study suggests that the accuracy of RF algorithm is higher than that of SVM. (C,D) Comparison of the specificity and sensitivity of RF algorithm and SVM algorithm. (E,F) RF method was used to screen four signature genes (GEM, TPM4, SNTB1, SLC6A8). (G) Correlation analysis of IL6 with GEM, TPM4, SNTB1, and SLC6A8 genes. (H) Correlation analysis of IL9 with TPM4, SLC6A8, SNTB1, and GEM genes. *, P<0.05; **, P<0.01; ***, P<0.001. Con, normal control; RF, random forest; SVM, support vector machine; abs (cor), correlation coefficient; EMT, epithelial-mesenchymal transition; DEGs, differentially expressed genes.

Figure 7

IL6 and IL9 genes and immune cell correlation analysis. (A) Heat map of immune cell differential analysis (asthma vs. non-asthma) in the GSE41861 dataset. (B) Histogram of immune cell differential analysis (asthma vs. non-asthma) in GSE41861 dataset. (C) IL6-immune cell correlation analysis. (D) IL6 gene correlation analysis with monocytes. (E) Correlation analysis of IL6 gene and mast cells activated. NK, natural killer; con, normal control; abs (cor), correlation coefficient.

Figure 8

Identification and characterization of regulatory mechanisms of IL6 and IL9 genes. (A) Enrichment analysis of IL6 gene in GSE63142 dataset. (B) Enrichment analysis of IL9 gene in GSE63142 dataset. (C) Construction of ceRNA network of IL6 and IL9 genes. KEGG, Kyoto Encyclopedia of Genes and Genomes; ceRNA, competing endogenous RNA.