Page 228 - 北京京煤集团总医院第十届·2022学术年会论文集

北京京煤集团总医院 第十届·2022 学术年会论文集

pathway enrichment analyses with a cutoff of P < 0.05. The GO functional analyses included BP,

CC, and MF.

2.4. PPI network
The PPI network was built using the STRING database (17) with a threshold of confidence

score > 0.4 to analyze the protein–protein interactions between the overlapped DEGs, and the PPI
network was shown by Cytoscape [18]. In addition, based on the maximal clique centrality

algorithm in the Cytohubba plug-in of Cytoscape [18], hub genes were identified from the PPI

network.

2.5. Identification of transcription factor (TF)-gene, miRNAs-gene interaction

The NetworkAnalyst website [19] was used to analyze the TFs and miRNAs of the overlapped

DEGs, and the TF-gene and miRNA-gene interaction networks were built.

2.6. Drug–gene interaction and related diseases

The overlapped DEGs also served as promising targets for searching drugs through the

DSigDB database [20] with an adjusted cutoff value, P < 0.05. In addition, the overlapping DEGs

were input into the DisGeNET database [21] to investigate the diseases associated with overlapping

DEGs.

3. Results

3.1. DEGs in COVID-19 and asthma
A total of 2,486 DEGs were screened between the SARS-CoV-2 uninfected and infected groups,

containing 1,442 upregulated and 1,044 downregulated DEGs (Figure 2A). A total of 1,052 DEGs

were identified from patients with severe asthma and healthy controls, comprising 466 upregulated

and 586 downregulated DEGs (Figure 2B). Moreover, 135 overlapped DEGs were obtained by

determining the DEG intersection between the GSE147507 and GSE143303 datasets (Figure 2C).

Figure 2. Identification of differentially expressed genes (DEGs). A: Volcano plot of DEGs in

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