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. 2022 Mar 22:13:829841.
doi: 10.3389/fgene.2022.829841. eCollection 2022.

Identification of Novel Biomarkers With Diagnostic Value and Immune Infiltration in Burn Injury

Sitong Zhou  1 , Kangchun Wang  2 , Jingru Wang  3 , Jia He  3 , Wenlian Zheng  4 , Chengmin Long  4 , Xiaodong Chen  3 , Ronghua Yang  5
Affiliations

Identification of Novel Biomarkers With Diagnostic Value and Immune Infiltration in Burn Injury

Sitong Zhou et al. Front Genet. .

Abstract

Burn injury is an intractable problem in the field of surgery where screening relevant target genes and exploring pathological mechanisms through bioinformatic methods has become a necessity. Herein, we integrated three burn injury mRNA microarray datasets from the Gene Expression Omnibus database to analyze the hub differentially expressed genes (DEGs) between burn injury patient samples and healthy human samples; we conducted multiple functional enrichment analyses and constructed the protein-protein interaction (PPI) network. Finally, we evaluated the immune infiltration in the burn injury microenvironment. A total of 84 intersection DEGs (32 upregulated and 52 downregulated) were screened in burn injury patients via integrated analyses. Upregulated genes were primarily enriched in regulation of T cell activation, regulation of response to DNA damage stimulus, positive regulation of innate immune response, positive regulation of defense response. We also identified 10 hub genes from the PPI network (CCNB2, MYO10, TTK, POLQ, VASP, TIMP1, CDK16, MMP1, ZYX, and PKMYT1). Next, we found that 22 immune cells were substantially changed during the burn injury by CIBERSORT. In addition, we verified that VASP and POLQ are two novel diagnostic markers in burn processes with high diagnostic efficacy via immunohistochemistry. In summary, we identified several key genes involved in burn injury and provided a favorable basis for elucidating the molecular mechanisms of burn injury through comprehensive bioinformatic analysis.

Keywords: CIBERSORT; burn injury; functional enrichment analysis; immune infiltration; inflammation.

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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
Principal component analysis and Venn plots of DEGs. (A) PCA results of GSE8056 dataset; (B) PCA results of GSE19743 dataset; (C) PCA results of GSE37069 dataset; (D) Venn plot of upregulated DEGs; (E) Venn plot of downregulated DEGs.
FIGURE 2
FIGURE 2
Differential expression analysis in three datasets. (A) The gene expression heatmap of important DEGs in the GSE8056 dataset; (B) the volcano plot of the differential expression analysis in the GSE8056 dataset; (C) the gene expression heatmap of important DEGs in the GSE19743 dataset; (D) volcano plot of differential expression analysis in GSE19743 dataset; (E) the gene expression heatmap of important DEGs in the GSE37069 dataset; (F) volcano plot of differential expression analysis in GSE37069 dataset.
FIGURE 3
FIGURE 3
Analysis results of functional enrichment. (A,B) GO analysis of up-regulated DEGs; (C) enrichment analysis of the KEGG of up-regulated DEGs; (D) enrichment analysis of the REACTOME pathway of up-regulated DEGs; (E,F) GO analysis of down-regulated DEGs.
FIGURE 4
FIGURE 4
Comprehensive database analysis. (A,B) Biological functions of upregulated genes analyzed through Metascape database; (C) tissue and cell characteristics of upregulated genes in PaGenBase database; (D) enrichment of transcriptional regulators of upregulated genes in TRRUST database; (E) DisGeNET database enrichment analysis of diseases involving upregulated genes; (F,G) Biological functions of downregulated genes analyzed through Metascape database; (H) DisGeNET database enrichment analysis of diseases involving downregulated genes; (I) enrichment of transcriptional regulators of downregulated genes in TRRUST database.
FIGURE 5
FIGURE 5
PPI network of upregulated DEGs and hub genes. (A,B) PPI network of up-regulation DEGs; (C) identified hub genes by CytoHubba.
FIGURE 6
FIGURE 6
GSEA enrichment analysis. (A-J) 10 positively correlated pathways in burn injury; (K-T) 10 negatively correlated pathways in burn injury.
FIGURE 7
FIGURE 7
(A–J) Comparison of the expression levels of 10 hub genes between normal and burn injury groups.
FIGURE 8
FIGURE 8
(A–J) Receiver operating characteristic curves of 10 hub genes in PPI network.
FIGURE 9
FIGURE 9
Immune infiltration analysis in the three datasets. (A) The expression heatmap of 22 immune cells in GSE37069; (B) the expression heatmap of 22 immune cells in GSE8056; (C) the expression heatmap of 22 immune cells in GSE19743; (D) correlation heatmap of immune cells in burn injury.
FIGURE 10
FIGURE 10
Immunohistochemistry staining and histologic scoring. IHC staining demonstrated that VASP (A) and POLQ (B) showed high expression; (C) results of histologic scoring and analysis.
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