Bioinformatics analysis to screen key genes in septic-induced acute lung injury

doi:10.3877/cma.j.issn.1674-6880.2022.05.002

Abstract

Abstract:

Objective

To screen key genes in sepsis-induced acute lung injury (ALI) by bioinformatics analysis.

Methods

The GSE10474 dataset was downloaded from the gene expression omnibus (GEO). The dataset included gene data of 13 patients with sepsis-induced ALI (ALI group) and 21 patients with sepsis (sepsis group). The limma package was used to screen differentially expressed genes between the two groups. Then gene ontology (GO) analysis and Kyoto encyclopedia of genes and genomes (KEEG) enrichment analysis were performed on these differentially expressed genes. The protein-protein interaction (PPI) network was established using the STRING database to identify the top 10 hub genes.

Results

A total of 115 differentially expressed genes were identified from the GSE10474 dataset, including 65 up-regulated genes and 50 down-regulated genes. GO analysis showed that the differentially expressed genes of biological processes were mainly enriched in metal ion homeostasis, oxidative stress, and ionizing radiation. The cellular components were mainly concentrated in biofilms such as vacuolar membrane, Golgi apparatus, endoplasmic reticulum membrane, and lysosomal membrane. These genes were mainly associated with molecular functions such as biological transmembrane, ubiquitin-conjugating enzyme activity, protein tyrosine, serine and threonine kinases binding protein activity, and protein kinase inhibitory activity. KEGG enrichment analysis showed that the differentially expressed genes were mainly concentrated in the phospholipase signaling pathway, insulin signaling pathway, and T cell-mediated immune response and immune-related signaling pathways. The PPI network screened out the top 10 hub genes, including CD4, CD74, myeloid cell nuclear differentiation antigen (MNDA), triggering receptor expressed on myeloid cells 1 (TREM1), human leukocyte antigen DRA (HLA-DRA), cytohesin 1 interacting protein (CYTIP), coagulation factor XⅢA chain (F13A1), cystatin F (CST7), mitogen-activated protein kinase 1 (MAPK1), and cyclin dependent kinase inhibitor 1A (CDKN1A).

Conclusion

CD4, CD74, MNDA, TREM1, HLA-DRA, CYTIP, F13A1, CST7, MAPK1, and CDKN1A are key genes for sepsis-induced ALI, which can be used as new targets for clinical treatment and drug development.

Key words: Sepsis, Acute lung injury, Genes, Computational biology

Zhehao Liang, Mingsun Fang, Hongyi Hu, Tao Tao, Xiaoping Xu, Huaqin Sun. Bioinformatics analysis to screen key genes in septic-induced acute lung injury[J]. Chinese Journal of Critical Care Medicine(Electronic Edition), 2022, 15(05): 360-366.

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URL: https://zhwzzyxzz.cma-cmc.com.cn/EN/10.3877/cma.j.issn.1674-6880.2022.05.002

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