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. 2021 Dec 17;26(1):146.
doi: 10.1186/s40001-021-00609-4.

An integrative bioinformatics analysis for identifying hub genes associated with infection of lung samples in patients infected with SARS-CoV-2

Tian-Ao Xie #  1   2 , Zhi-Jian He #  1   2 , Chuan Liang #  1   2 , Hao-Neng Dong  1   2 , Jie Zhou  1   2 , Shu-Jin Fan  1   2 , Xu-Guang Guo  3   4   5   6
Affiliations

An integrative bioinformatics analysis for identifying hub genes associated with infection of lung samples in patients infected with SARS-CoV-2

Tian-Ao Xie et al. Eur J Med Res. .

Abstract

Background: At the end of 2019, the world witnessed the emergence and ravages of a viral infection induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Also known as the coronavirus disease 2019 (COVID-19), it has been identified as a public health emergency of international concern (PHEIC) by the World Health Organization (WHO) because of its severity.

Methods: The gene data of 51 samples were extracted from the GSE150316 and GSE147507 data set and then processed by means of the programming language R, through which the differentially expressed genes (DEGs) that meet the standards were screened. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on the selected DEGs to understand the functions and approaches of DEGs. The online tool STRING was employed to construct a protein-protein interaction (PPI) network of DEGs and, in turn, to identify hub genes.

Results: A total of 52 intersection genes were obtained through DEG identification. Through the GO analysis, we realized that the biological processes (BPs) that have the deepest impact on the human body after SARS-CoV-2 infection are various immune responses. By using STRING to construct a PPI network, 10 hub genes were identified, including IFIH1, DDX58, ISG15, EGR1, OASL, SAMD9, SAMD9L, XAF1, IFITM1, and TNFSF10.

Conclusion: The results of this study will hopefully provide guidance for future studies on the pathophysiological mechanism of SARS-CoV-2 infection.

Keywords: Differentially expressed genes; Hub genes; Protein–protein interactions network; SARS-CoV-2.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The intersection DEGs of GSE147507 and GSE150316
Fig. 2
Fig. 2
Volcano map and heat map of DEGs. A, B The volcano map and heat map of DEGs that were plotted for GSE147507. C D The volcano map and heat map of DEGs that were plotted for GSE150316. The X axis represents the logarithm of the fold change. The Y axis represents the negative value of the logarithm of the p value. Red dots represent up-regulated genes that meet the screening criteria, and blue dots represent down-regulated genes that meet the screening criteria (A, C). Gene expression data are converted into a data matrix. Each column represents the genetic data of a sample, and each row represents a gene. The color of each cell represents the expression level, and there are references to expression levels in different colors in the upper right corner of the figure (B, D)
Fig. 3
Fig. 3
The top 10 enriched terms of GO analysis (BP, CC, MF) in the system matrix file GSE147507 (A) and GSE150316 (B)
Fig. 4
Fig. 4
Functional and pathway enrichment analyses of DEGs in the system matrix file GSE147507 and GSE150316
Fig. 5
Fig. 5
The PPI network of GSE147507
Fig. 6
Fig. 6
The PPI network of GSE150316
Fig. 7
Fig. 7
The intersection PPI network of GSE147507 and GSE150316
Fig. 8
Fig. 8
The hub genes of GSE147507 and GSE150316
Fig. 9
Fig. 9
The verification of hub genes
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