Construction of an immunorelated protein-protein interaction network for clarifying the mechanism of burn

Identification of Key Genes in Severe Burns by Using Weighted Gene Coexpression Network Analysis

The aims of this work were to explore the use of weighted gene coexpression network analysis (WGCNA) for identifying the key genes in severe burns and to provide a reference for finding therapeutic targets for burn wounds. The GSE8056 dataset was selected from the gene expression database of the US National Center for Biotechnology Information for analysis, and a WGCNA network was constructed to screen differentially expressed genes (DEGs). Gene Ontology and pathway enrichment of DGEs were analyzed, and protein interaction network was constructed. A burn mouse model was constructed, and the burn tissue was taken to identify the expression levels of differentially expressed genes. The results showed that the optimal soft threshold for constructing the WGCNA network was 9. 10 coexpressed gene modules were identified, among which the green, brown, and gray modules had the largest number of burn-related genes. The DEGs were mainly related to immune cell activation, inflammatory response, and immune response, and they were enriched in PD-1/PD-L1, Toll-like receptor, p53, and nuclear factor-kappa B (NF-κB) signaling pathways. 5 DEGs were screened and identified, namely, Jun protooncogene (JUN), signal transducer and activator of transcription 1 (STAT1), BCL2 apoptosis regulator (Bcl2), matrix metallopeptidase 9 (MMP9), and Toll-like receptor 2 (TLR2). Compared with skin tissue of normal mouse, the messenger ribose nucleic acid (mRNA) and protein expression levels (PEL) of STAT1 and Bcl2 in burn tissue were greatly decreased, while those of JUN, MMP9, and TLR2 were increased obviously (p < 0.05). In conclusion, STAT1, Bcl2, JUN, MMP9, and TLR2 can be potential biological targets for the treatment of severe burn wounds.

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

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.

Identifying changes in immune cells and constructing prognostic models using immune-related genes in post-burn immunosuppression

BACKGROUND

Burn patients are prone to infection as well as immunosuppression, which is a significant cause of death. Currently, there is a lack of prognostic biomarkers for immunosuppression in burn patients. This study was conducted to identify immune-related genes that are prognosis biomarkers in post-burn immunosuppression and potential targets for immunotherapy.

METHODS

We downloaded the gene expression profiles and clinical data of 213 burn patients and 79 healthy samples from the Gene Expression Omnibus (GEO) database. Immune infiltration analysis was used to identify the proportion of circulating immune cells. Functional enrichment analyses were carried out to identify immune-related genes that were used to build miRNA-mRNA networks to screen key genes. Next, we carried out correlation analysis between immune cells and key genes that were then used to construct logistic regression models in GSE77791 and were validated in GSE19743. Finally, we determined the expression of key genes in burn patients using quantitative reverse transcription polymerase chain reaction (qRT-PCR).

RESULTS

A total of 745 differently expressed genes were screened out: 299 were up-regulated and 446 were down-regulated. The number of Th-cells (CD4+) decreased while neutrophils increased in burn patients. The enrichment analysis showed that down-regulated genes were enriched in the T-cell activation pathway, while up-regulated genes were enriched in neutrophil activation response in burn patients. We screened out key genes (NFATC2, RORA, and CAMK4) that could be regulated by miRNA. The expression of key genes was related to the proportion of Th-cells (CD4+) and survival, and was an excellent predictor of prognosis in burns with an area under the curve (AUC) value of 0.945. Finally, we determined that NFATC2, RORA, and CAMK4 were down-regulated in burn patients.

CONCLUSION

We found that NFATC2, RORA, and CAMK4 were likely prognostic biomarkers in post-burn immunosuppression and potential immunotherapeutic targets to convert Th-cell dysfunction.

Altered Genes and Biological Functions in Response to Severe Burns

Severe burns are acute wounds caused by local heat exposure, resulting in life-threatening systemic effects and poor survival. However, the specific molecular mechanisms remain unclear. First, we downloaded gene expression data related to severe burns from the GEO database (GSE19743, GSE37069, and GSE77791). Then, a gene expression analysis was performed to identify differentially expressed genes (DEGs) and construct protein-protein interaction (PPI) network. The molecular mechanism was identified by enrichment analysis and Gene Set Enrichment Analysis. In addition, STEM software was used to screen for genes persistently expressed during response to severe burns, and receiver operating characteristic (ROC) curve was used to identify key DEGs. A total of 2631 upregulated and 3451 downregulated DEGs were identified. PPI network analysis clustered these DEGs into 13 modules. Importantly, module genes mostly related with immune responses and metabolism. In addition, we identified genes persistently altered during the response to severe burns corresponding to survival and death status. Among the genes with high area under the ROC curve in the PPI network gene, CCL5 and LCK were identified as key DEGs, which may affect the prognosis of burn patients. Gene set variation analysis showed that the immune response was inhibited and several types of immune cells were decreased, while the metabolic response was enhanced. The results showed that persistent gene expression changes occur in response to severe burns, which may underlie chronic alterations in physiological pathways. Identifying the key altered genes may reveal potential therapeutic targets for mitigating the effects of severe burns.

Genome-wide comparisons of gene expression in adult versus elderly burn patients

Purpose

Mortality and morbidity rates of elderly burn patients remain high despite numerous advancements in modern burn care. While prior studies have offered first insights on the biochemical changes in elderly burn patients compared to adults, the underlying cellular responses remain largely unknown. In this study, we aim to characterize the transcriptome of elderly burn patients and compare it to adult burn patients to obtain insights into the underlying molecular responses post-burn and to elucidate the effect of advanced age on the acute burn response.

Materials and methods

Microarray data obtained from the Glue Grant Trauma-Related Database was obtained from blood specimens for ten elderly patients (n = 10), each with a set of two sex and total body surface area (TBSA) matched adult controls (n = 20), during the acute phase post-burn. Adult and elderly demographics and clinical outcomes were contrasted using using the Chi-Square test, Fisher’s Exact Test, or two-sample t-tests, as appropriate (p<0.05). Enrichment and heat maps were generated to compare gene expression in elderly versus adult burn patients.

Results

Supervised analysis identified multiple genes that were differentially expressed between the elderly and adult groups. Pathway analysis and heatmap generation suggest that elderly patients share a distinct hypo-inflammatory response in the acute post-burn phase with downregulation of a number of immune-related pathways, including those related to antigen processing, specifically via MHC class I, ubiquitination and proteasome degradation (p<0.001, FDR < .001). Cell signalling pathways, such as NF-κB, C-type lectin receptor, and T cell receptor signalling were also significantly downregulated in elderly burn patients, as well as those relating to antiviral immunity (p<0.001, FDR < .001). Many genes which were observed to be upregulated in elderly patients with high TBSA burn injuries were associated with destruction-related cellular pathways such as complement activation and immunoglobulin production (p<0.005, FDR <0.01).

Conclusions

The altered inflammatory and immune responses at the transcriptome level in elderly patients after burn are indicative of a failure in elderly burn patients to initiate an appropriate inflammatory and stress response during the acute phase post-burn.

Time series analysis of gene changes and processes after burn with human gene expression profiles

Severe burns might be followed by severe infection associated with high mortality. In this study, we aimed to identify changes in genes and processes across time points after burn via analyzing time series gene expression profiles in burn patients and control from the Gene Expression Omnibus (GEO). Patients were classified into four groups according to time after burn and weighted gene co-expression network analysis (WGCNA) obtained three gene modules including magenta, yellow and greenyellow modules that significantly correlated positively with time after burn. We also identified four groups of differentially expressed genes (DEGs) in samples at 0-1d, 1-2d, 2-4d, and 4-7d after burn compared with controls. Functional enrichment analysis of those DEGs indicated significant enrichment of inflammatory/immune related processes throughout time points after burn, while, samples at later time points were also closely associated with cell activation regulation related processes. Short time series expression analysis of overlapping genes among the four lists of DEGs screened out two temporal gene expression profiles that exhibited decreasing and increasing expression trend across times after burn, and genes contained in those two profiles might be related to pathologic changes after severe burn.

[Screening of biomarkers related with leukocyte responses early after burn injury in mice by differential gene expression profiling]

OBJECTIVE

To screen the genes related with leukocyte responses in mice early after burn injury by bioinformatic analysis of the gene expression profiling data.

METHODS

The gene expression profiles were obtained from GEO (GSE7404, Mouse musculus, 25% TBSA, full-thickness) database. T test, fold changes and GO functional enrichment analysis were used to identify the differentially expressed genes (DEGs) related to leukocyte responses to burns; the interacting genes were transferred to STRING to construct the protein-protein interaction (PPI) network. Biological annotation of the sub-networks was executed using the software Cytoscape. Real-time PCR and Western blotting were used to verify the DEGs in mice.

RESULTS

In mice at 1 day post-burn, a total of 658 genes were up-regulated and 1167 were down-regulated. PPI network and module analysis suggested that some of the genes (Stat1, Cdk1, Cd19, Lck and Jun) may play critical roles in the PPI network post-burn. Real-time PCR and Western blotting results in mice were consistent with those of bioinformatic analysis of Stat1, Cdk1 and Jun.

CONCLUSION

Stat1, Cdk1 and Jun might be critical players in the development of leukocyte response in mice early after burn injury. Our finding provides new insights into the pathogenesis of leukocyte response to burn injury and identifies several biomarkers as potential targets for burn treatment.