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. 2022 Jul 28:13:937832.
doi: 10.3389/fimmu.2022.937832. eCollection 2022.

Identification of pyroptosis-related subtypes and establishment of prognostic model and immune characteristics in asthma

Fan Yang  1   2 , Tieshan Wang  3 , Peizheng Yan  4 , Wanyang Li  5 , Jingwei Kong  1   2 , Yuhan Zong  1   2 , Xiang Chao  1 , Weijie Li  6 , Xiaoshan Zhao  7 , Ji Wang  2
Affiliations

Identification of pyroptosis-related subtypes and establishment of prognostic model and immune characteristics in asthma

Fan Yang et al. Front Immunol. .

Abstract

Background: Although studies have shown that cell pyroptosis is involved in the progression of asthma, a systematic analysis of the clinical significance of pyroptosis-related genes (PRGs) cooperating with immune cells in asthma patients is still lacking.

Methods: Transcriptome sequencing datasets from patients with different disease courses were used to screen pyroptosis-related differentially expressed genes and perform biological function analysis. Clustering based on K-means unsupervised clustering method is performed to identify pyroptosis-related subtypes in asthma and explore biological functional characteristics of poorly controlled subtypes. Diagnostic markers between subtypes were screened and validated using an asthma mouse model. The infiltration of immune cells in airway epithelium was evaluated based on CIBERSORT, and the correlation between diagnostic markers and immune cells was analyzed. Finally, a risk prediction model was established and experimentally verified using differentially expressed genes between pyroptosis subtypes in combination with asthma control. The cMAP database and molecular docking were utilized to predict potential therapeutic drugs.

Results: Nineteen differentially expressed PRGs and two subtypes were identified between patients with mild-to-moderate and severe asthma conditions. Significant differences were observed in asthma control and FEV1 reversibility between the two subtypes. Poor control subtypes were closely related to glucocorticoid resistance and airway remodeling. BNIP3 was identified as a diagnostic marker and associated with immune cell infiltration such as, M2 macrophages. The risk prediction model containing four genes has accurate classification efficiency and prediction value. Small molecules obtained from the cMAP database that may have therapeutic effects on asthma are mainly DPP4 inhibitors.

Conclusion: Pyroptosis and its mediated immune phenotype are crucial in the occurrence, development, and prognosis of asthma. The predictive models and drugs developed on the basis of PRGs may provide new solutions for the management of asthma.

Keywords: DPP4; asthma; immune cell; prognostic model; pyroptosis-related genes.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Expression and biological function of PRG in mild to moderate asthma and severe asthma. (A) 19 differentially expressed PRGs between mild to moderate and severe asthma. (B) Co-expression network of 19 PRGs. (C) GO and KEGG enrichment analysis of differentially expressed PRG. *P < 0.05 , **P < 0.01.
Figure 2
Figure 2
Two different pyroptosis-related subtypes identified in asthma by unsupervised clustering of 19 PRGs. (A) Heatmap of the matrix of co-occurrence proportions for asthma samples. (B) Kaplan–Meier curves of different gene subtypes ACQ Control and FEV1 reversibility. (C) Composite heatmap showing the relationship between the expression levels of 22 PRGs and clinical features. (D, E) GSEA analysis of C1 and C2 subtypes.
Figure 3
Figure 3
Identification and functional enrichment analysis of asthma control related genes in C1 subtype. (A) WGCNA of the C1 subtype to obtain a cluster dendrogram of coexpressed genes. (B) Analysis of the scale free ft index and analysis of the mean connectivity for various soft – theresholding powers. (C) Module – trait relationships for C1 subtypes. Each module contains the corresponding correlation and P-value. (D) GO and KEGG functional enrichment analysis of genes in MEpink4 module.
Figure 4
Figure 4
Screening of pyroptosis-related diagnostic markers. (A) Volcano map of DEGs. red represents up-regulated differential genes, black represents no significant difference genes, and green represents down-regulated differential genes. (B) LASSO logistic regression algorithm to screen diagnostic markers. (C) Difference of BNIP3 gene expression between C1 and C2 subtypes. (D, E) Differential expression of BNIP3 between normal and asthmatic mice. (F) Differential expression of BNIP3 mRNA between normal and asthmatic mice. (G) ROC curve for verifying the efficacy of BNIP3 gene diagnosis. (H) Enrichment difference of Th1 and Th2 cell differentiation pathway between BNIP3 high expression group and low expression group. (I) Functional enrichment analysis of DEGs between BNIP3 high expression group and low expression group. (J, K) GSEA enrichment analysis of DEGs between BNIP3 high expression group and low expression group. **P < 0.01.
Figure 5
Figure 5
The difference of immune cell infiltration among subtypes. (A) The histogram of the infiltration ratio of 22 kinds of immune cells in C1 group and C2 subtype samples. (B) The correlation among 22 kinds of immune cells. (C) The lollipop diagram shows the correlation between BNIP3 gene and 22 kinds of immune cells, and red marks indicate P < 0.05.
Figure 6
Figure 6
Correlation of BNIP3 with immune cell infiltration and its gene expression regulatory network. (A) Mountain diagram showing the correlation between the BNIP3 gene and immune cells. (B) The intersection of the prediction results of the three databases. (C) CeRNA networks involved in the regulation of BNIP3 gene expression.
Figure 7
Figure 7
The prognostic model constructed by differentially expressed genes from different pyroptosis patterns can distinguish between high- and low-risk patients with asthma. (A) DEGs associated with asthma control status. Red and green colors represent high- and low-risk genes, respectively. (B, C) Distribution of LASSO coefficients for DEGs. Tenfold cross-validation for tuning parameter selection in the LASSO regression. Dotted vertical lines are drawn at the optimal values by minimum criteria and 1 − SE criteria. (D) Kaplan–Meier curves of ACQ control and FEV1 reversibility in patients with different risk groups. (E) P-score distribution of patients with different risks. (F) The distribution of FEV1 reversibility and asthma control among different risk samples. (G, H) PCA and t-SNE was used to determine whether the samples could be grouped correctly based on the P-score. (I) Multivariate Cox regression analysis of P-score was shown by forest plot.
Figure 8
Figure 8
Immune status of patients at different risks and prediction of potential therapeutic drugs. (A) The expression differences of P-score constituent genes among different risk groups. (B) The difference of expression of P-score constituent genes between normal mice and asthmatic mice. (C) Correlation between potential therapeutic drugs and corresponding targets. (D) Binding conformation of DPP4 and diprotin-A (binding energy = −6.4 kcal/mol). (E) Binding conformation of DPP4 and sitagliptin (binding energy = −7.8 kcal/mol). *P < 0.05, **P < 0.01, ***P < 0.001.
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