The Identification and Clinical Value Evaluation of CYCS Related to Asthma through Bioinformatics Analysis and Functional Experiments

Abstract

Background: Asthma is one of the most common respiratory diseases and one of the largest burdens of health care resources across the world. This study is aimed at using bioinformatics methods to find effective clinical indicators for asthma and conducting experimental validation.

Methods: We downloaded GSE64913 data and performed differentially expressed gene (DEG) screening. Weighted gene coexpression network analysis (WGCNA) on DEGs was applied to identify key module most associated with asthma for protein-protein interaction (PPI) analysis. According to the degree value, ten genes were obtained and subjected to expression analysis and receiver operating characteristic (ROC) analysis. Next, key genes were screened for expression analysis and immunological analysis. Finally, cell counting kit-8 (CCK-8) and qRT-PCR were also conducted to observe the influence of hub gene on cell proliferation and inflammatory cytokines.

Results: From the GSE64913 dataset, 711 upregulated and 684 downregulated DEGs were found. In WGCNA, the top 10 genes in the key module were examined by expression analysis in asthma, and CYCS was determined as an asthma-related oncogene with a good predictive ability for the prognosis of asthmatic patients. CYCS is significantly associated with immune cells, such as HHLA2, IDO1, TGFBR1, and CCL18 and promoted the proliferation of asthmatic cells in vitro.

Conclusion: CYCS plays an oncogenic role in the pathophysiology of asthma, indicating that this gene may become a novel diagnostic biomarker and promising target of asthma treatment.

Figure 1

Identification and functional enrichment analysis of DEGs in the GSE64913 dataset. (a) 1395° cluster heat map, blue represents the asthma group, and orange represents the control group. Red refers to upregulation, and blue refers to downregulation. (b, c) GO (including BP, CC, and MF) and KEGG enrichment analysis of upregulated DEGs. (d, e) GO and KEGG enrichment analysis of downregulated DEGs.

Figure 2

WGCNA analysis of DEGs in the GSE64913 dataset. (a) Sample clustering to detect outliers. (b) Scale-free topology model (top) and average connectivity (bottom) for finding the soft-threshold capability. Power is 20. (c) Hierarchical dendrogram showing coexpression modules recognized by WGCNA. Each leaf on the tree represents a gene. The main tree is divided into 7 modules according to the eigengene calculations, and each module is highlighted with a different color. (d) Clustering of all modules. (e) Heat map showing the relationship between different modules and clinical features.

Figure 3

Screening of candidate key genes, expression level analysis, and ROC diagnostic value analysis. (a) PPI network graph of genes in brown modules, including 40 nodes and 45 edges. (b) Degree ranking of genes by Cytohubba software. (c) PPI network of the 10 genes with the highest height values. (d) KEGG enrichment analysis of the top 10 genes. (e) Expression levels of 10 genes, listed by ANXA8, ATF4, CD44, CYCS, DDIT3, FKBP5, LDHA, PMAIP1, S100A2, and SFN. (f) ROC analysis on the top 10 genes. The abscissa is 1-specificity (FPR), and the ordinate is sensitivity (TPR). The closer the curve is to the upper left corner, the higher the prediction accuracy. ^∗P < 0.05, ^∗∗P < 0.01, and ^∗∗∗P < 0.001.

Figure 4

Validation of CYCS expression in tumors and analysis of immune levels. (a) Expression of CYCS in human tissues. (b) CYCS expression in TCGA tumors. (c) Comparative analysis of CYCS expression levels in normal and tumor tissues. (d) Heat map of the correlation analysis between CYCS and immune infiltration levels in pan-cancer. (e) Correlation analysis between CYCS and immunosuppressants. (f) Correlation analysis between CYCS and chemokines. (g) Correlation analysis between CYCS and immunostimulants. ^∗P < 0.05, ^∗∗P < 0.01, and ^∗∗∗P < 0.001.

Figure 5

The differential expression of CYCS regulates the proliferation and cytokine expression levels of 16-HBE cells. (a) Confirmation of knockdown transfection efficiency of si-CYCS #1 and si-CYCS #2 using qRT-PCR. (b) Si-CYCS #2 promotes cell proliferation in 16-HBE cells. (c) The transfection efficiency of CYCS #1 and CYCS #2 overexpression was confirmed using qRT-PCR. (d) Over-CYCS #1 inhibits cell proliferation of 16-HBE cells. (e–h) Expression of CCL-17, IL-5, IL-8, and COX-2 expressions in cells with si-CYCS. (i–l) CCL-17, IL-5, IL-8, and COX-2 expressions in over-CYCS cells. ^∗P < 0.05, and ^∗∗P < 0.01.