Identification of differentially expressed oxidative stress-related genes in major burned patients and potential mechanisms based on hub genes

doi:10.21203/rs.3.rs-4857480/v1

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Identification of differentially expressed oxidative stress-related genes in major burned patients and potential mechanisms based on hub genes

https://doi.org/10.21203/rs.3.rs-4857480/v1

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Background: Burns are extremely destructive injuries to the human body. Once a major burn occurs, it can lead to long-term disabilities, psychological burdens, and significant loss of human resources and economic losses. Extensive burns can cause severe oxidative stress, systemic inflammatory response, and a hypermetabolic state. Besides traditional symptomatic treatment, there is an urgent need for new approaches to address oxidative stress and internal environment disturbance in burn injuries.

Methods: Based on the GEO database and the Limma R package, differential gene expression analysis was performed to identify genes differentially expressed in severe burns and genes associated with oxidative stress. The Gene Ontology (GO) database was used to analyze the cellular functions of these oxidative stress-related genes, and weighted gene co-expression networks analysis (WGCNA) and protein-protein interaction (PPI) networks were constructed. Receiver operating characteristic (ROC) curves were then constructed to identify hub genes in the networks. A diagnostic model based on these central genes was established using the least absolute shrinkage and selection operator (LASSO) analysis and ROC analysis. Immune-related functions were assessed by evaluating the correlation between the expression of hub genes and immune cell infiltration scores. Additionally, drug-gene interaction databases were used to predict target drugs, and miRNet was used to predict the regulation of miRNA and transcription factors.

Results: Through differential analysis, a total of 2,899 differentially expressed genes, 4,936 WGCNA module genes, and 437 oxidative stress-related genes were identified, resulting in 44 candidate genes. Ten hub genes (GCLM, MMP9, BCL2, IL10, SIRT1, NME8, PINK1, GCLC, EPAS1, PARK7) were identified. These central genes were found to be enriched in GO annotations related to “starch and sugar metabolism,” “lysosomes,” and “actin cytoskeleton regulation.” Additionally, it is predicted that 78 drugs can target GCLM, MMP9, BCL2, IL10, SIRT1, NME8, PINK1, GCLC, EPAS1, PARK7, including cisplatin, curcumin, and bevacizumab. A hub gene-miRNA regulatory network with 168 miRNAs and a hub gene-transcription factor (TF) network with 15 TFs were also constructed. These hub genes may serve as biomarkers for the assessment and treatment of oxidative stress in major burns, and provide clues for potential new therapeutic targets.

Conclusion: Based on the common genes associated with major burns and oxidative stress in this study, 10 target genes were proposed. These candidate genes may provide new insights into potential mechanisms for exploring the increased risk of burn severity changes caused by oxidative stress at the transcriptional level.

Biological sciences/Immunology

Health sciences/Diseases

Health sciences/Medical research

Health sciences/Molecular medicine

oxidative stress

major burn

immune

LASSO

gene-miRNA regulatory network

Burns are one of the most destructive types of injuries. After a significant burn, the probability of infection increases due to the breakdown of the skin barrier, causing an increase in patient mortality rate and treatment costs. In recent years, the emergence of multidrug-resistant bacteria has made it more difficult to treat burns, making the treatment of major burns a highly challenging problem^[1].Major burns can easily trigger characteristic cardiovascular dysfunction known as burn shock. In addition, plasma leakage leads to reduced blood volume, peripheral circulation disorders, and accumulation of metabolic waste. This, in turn, further exacerbates inflammatory responses and increases the risk of multiorgan failure^[2].

Oxidative stress has been shown to be involved in many diseases, including atherosclerosis, chronic obstructive pulmonary disease (COPD), Alzheimer’s disease, and cancer, revealing the mechanism by which oxidants promote cellular damage.^[3] There are two main mechanisms by which oxidative stress leads to disease. Firstly, the production of peroxides during the oxidative stress reaction, such as •OH, ONOO-, and HOCl, directly oxidizes macromolecules including membrane lipids, structural proteins, enzymes, and nucleic acids, leading to abnormal cell function and cell death. Secondly, hydrogen peroxide (H2O2) acts as a second messenger, generating abnormal redox signaling^{[4, 5]}. In addition to its role in many chronic diseases, oxidative stress also plays a significant role in acute injuries, such as in inflammation and ischemia-reperfusion injury. In these situations, oxidative stress can interfere with multiple signaling pathways through various mechanisms, such as modifying proteins, promoting inflammation, inducing apoptosis, disrupting autophagic regulation, and impairing mitochondrial function, affecting a variety of biological processes^{[6, 7]}. Obviously,oxidative stress is an important factor in the progression of major burn injuries^[8]. It not only affects the changes in the patient’s condition but also relates to wound healing^[9], dysfunction of the intestinal mucosal barrier^[10], and heart failure after a burn injury^[11].

In this study, we systematically analyzed the RNA-seq data from blood samples of severely burned patients and healthy controls in the GEO database, conducted differential analysis, identified oxidative stress-related genes (OSRGs), and performed GO enrichment analysis and KEGG pathway analysis to analyze the cellular functions and pathways of these genes. We constructed a weighted gene co-expression network (WGCN) and a protein-protein interaction (PPI) network and identified the core genes using Cytoscape software. Based on these central genes, we established a diagnostic model using the least absolute shrinkage and selection operator (LASSO) and ROC analysis. In addition, we used the drug-gene interaction database to predict target drugs and miRNet to predict the regulatory miRNA and transcription factors. As a result, we identified a total of 2899 differentially expressed genes, 4936 WGCNA module genes, and 437 oxidative stress-related genes, resulting in 44 candidate genes. Among them, 10 hub genes were identified (GCLM, MMP9, BCL2, IL10, SIRT1, NME8, PINK1, GCLC, EPAS1, PARK7). These central genes were found to be enriched in GO annotations associated with “starch and sugar metabolism”, “lysosomes”, and “actin cytoskeleton regulation”. Additionally, based on predictions, 78 drugs can target these core genes, including cisplatin, curcumin, and bevacizumab. A central gene-miRNA regulatory network with 168 miRNAs and a central gene transcription factor (TF) network with 15 TFs were also constructed. These central genes may serve as biomarkers for assessing and treating oxidative stress in severe burns, providing insights for new therapeutic strategies.

Dataset preparation

Download all gene expression data for major burn from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). The GSE37069 dataset is based on the GPL570 platform (HG-U133_Plus_2; Affymetrix Human Genome U133 Plus 2.0 array) and includes a total of 590 samples, consisting of 553 blood samples from major burn patients and 37 from healthy controls. Another dataset, GSE19743, includes 114 blood samples from major burn patients and 63 from healthy controls. The raw expression data was processed and a expression matrix was created using R software (version 4.3.1, https://www.r-project.org/) and Bioconductor Packages. The raw data (CEL files) was preprocessed and normalized using the affy package in the R environment.

Identification of DEGs

Using |log2 fold change (FC)| > 0.5 as the filtering criterion, “Limma” R package is used to identify differentially expressed genes (DEGs) in GSE37069 dataset, where log FC > 0.5 represents Up and log FC < -0.5 represents Down. The DEG heatmap and volcano plot are generated using “Pheatmap” R package and “ggplot2” R package, respectively, to visualize the DEGs.

WGCNA analysis

Weighted Gene Co-expression Network Analysis (WGCNA)^[12] is used to identify modules of highly correlated genes, summarize the interconnections between modules, and associate them with external sample traits to identify candidate biomarkers or therapeutic targets. In our study, WGCNA was constructed using the R package “WGCNA” to identify modules with the highest correlation to immune cells in major burn patients. Firstly, we preprocessed the sample data and removed outliers. Then, we used the “WGCNA” package to construct a correlation matrix. The correlation matrix was transformed into an adjacency matrix using an optimal soft threshold, and a topological overlap matrix (TOM) was generated from the adjacency matrix. Genes with similar expression patterns were classified into gene modules using average linkage hierarchical clustering based on TOM-based dissimilarity measure. Gene modules were further analyzed using a selection criterion (|Cor| > 0.4).

Finally, intersect these module genes with differentially expressed genes (DEGs) and oxidative stress-related genes (OSRGs) to identify the common genes, which will be used for further analysis.

KEGG and GO enrichment analysis

In order to understand the enrichment of GO and KEGG pathways for the intersecting genes, this study utilized the “clusterProfiler” R package in R to conduct functional enrichment analysis of GO and KEGG pathways, assessing the biological processes (BP), cellular components (CC), molecular functions (MF), and resulting signaling pathways associated with the intersecting genes.

Protein–Protein Interaction Network construction

The online tool STRING (Search Tool for Retrieval of Interacting Genes), which is used to predict protein-protein interactions (PPI)^[13], was employed to construct a PPI network of selected genes. Interactions with a combined score of > 0.4 are considered statistically significant, and a protein interaction network graph was generated.

Obtaining hub genes

We utilized Cytoscape software (v3.8.2) to optimize the PPI network for improved visualization^[14]. The CytoHubba plugin (version 1.6) within Cytoscape was utilized to explore important genes in the PPI network. The top 10 genes with the highest number of interactions were identified as hub genes. These hub genes are likely to play a central role in the disease development process. We used the Degree score to calculate the hub genes.

Expression of hub genes and ROC curve analysis

We analyzed the expression of the 10 hub genes in major burn and normal samples in the GSE37069 dataset. ROC curve analysis was then performed to evaluate the diagnostic performance of these 10 hub genes, using an AUC value > 0.7 as the threshold for selecting diagnostic markers.

GSEA analysis

We selected genes with the AUC of ROC curve greater than 0.7 for Gene Set Enrichment Analysis (GSEA) to explore the molecular functions of the hub genes.

Hub gene expression levels and correlations between immune cell infiltration scores analysis

Based on the hub genes with the AUC of ROC curve greater than 0.7, we analyzed the correlation between these genes. In order to understand the role of these hub genes in inflammation in major burn, inflammation cell scoring and immune correlation analysis were conducted.

Regulatory network construction and potential drug prediction

We predicted the transcription factors (TFs) and miRNAs by accessing the miRNET website, a miRNA-centric network visual analysis platform (https://www.minet.ca/)^[15]. Subsequently, we visualized the results using the Cytoscape software. For potential drug predictions, we accessed the Dgidb database through the enrichment platform (https://dgidb.genome.wustl.edu/)^[16].

Multi-gene diagnostic models and independent validation analysis

The Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression analysis is a data mining method that filters out less important variables by applying an L1 penalty (lambda) to set their coefficients to zero, in order to select the most important variables and build the optimal classification model^[17]. Utilizing LASSO regression and ROC analysis, we constructed a diagnostic model based on the hub genes identified from the network. Then, we validated the performance of the model using the GEO dataset GSE19743.

Identification of DEGs and module genes.

In this study, the "Limma" R package was used to identify differentially expressed genes (DEGs) in the dataset GSE37069. The selection criteria were set as |log2 FC| > 0.575. Heatmaps and volcano plots were generated to visualize the differential gene expression(Fig. 1A-B). A total of 2899 DEGs were identified between the Major burn and control samples, and the top 10 genes with the largest differential expression were SLC2A3, CCDC84, MAPK14, KIAA1377, CR1, MMP9, BMX, B3GALT2, PYGL, and NR3C2. In the weighted gene co-expression network analysis, a tree diagram was constructed by incorporating the gene sets of all samples without outliers. The soft threshold was set to 11 (R^2 = 0.95) to construct a scale-free network, resulting in the identification of 18 modules(Fig. 2A-D). Based on the criterion (|Cor| > 0.4), the darkturquoise, red, and pink modules were selected, which included a total of 4936 genes for further analysis(Fig. 2E-G).

Acquisition and functional enrichment of intersection genes

The intersection of the obtained 2899 DEGs, 4936 module genes, and 437 OSRGs resulted in 44 intersection genes. A Venn diagram and Manhattan plot were created to visualize these intersection genes and their genomic positions(Fig. 3A). We also investigated the chromosomal locations of intersecting genes and visualized them(Fig. 3B,C). GO and KEGG analyses were applied to the cluster Profiler package to explore the potential molecular functions and mechanisms of the intersection genes(Fig. 3D-F). Bubble plots and bar charts were generated for GO and KEGG enrichment, revealing significant enrichment in 10 biological process (BP) terms, including regulation of oxidative stress − induced cell death, regulation of response to oxidative stress, cell death in response to oxidative stress, cellular response to hydrogen peroxide, cellular response to reactive oxygen species, response to hydrogen peroxide, response to reactive oxygen species, cellular response to chemical stress, cellular response to oxidative stress, response to oxidative stress, 10 cellular component (CC) termfs, including astrocyte projection, perinuclear endoplasmic reticulum, organelle envelope lumen, mitochondrial intermembrane space, glial cell projection, outer membrane, organelle outer membrane, mitochondrial outer membrane, membrane microdomain, membrane raf, and 10 molecular function (MF) terms, including DNA−(apurinic or apyrimidinic site) endonuclease activity, carbon − oxygen lyase activity, damaged DNA binding, scaffold protein binding, oxidoreductase activity, acting on peroxide as acceptor, deoxyribonuclease activity, DNA endonuclease activity, antioxidant activity, oxidoreductase activity, acting on a sulfur group of donors, DNA − binding transcription factor binding. The top five KEGG pathways were most enriched, including Glutathione metabolism, Chemical carcinogenesis − reactive oxygen species, Focal adhesion, Pathways of neurodegeneration − multiple diseases, MicroRNAs in cancer.

Identification of hub genes

In order to identify hub genes, we constructed a protein-protein interaction (PPI) network using the STRING website and performed analysis using Cytoscape(Fig. 4). We selected the top 10 hub genes with the highest number of connections, which are GCLM, MMP9, BCL2, IL10, SIRT1, NME8, PINK1, GCLC, EPAS1, and PARK7. Among these, SIRT1 and BCL2 have the closest connections to other parts of the network, followed by PKRK7 and MMP9.

Expression of hub genes and ROC curve analysis

All 10 hub genes have different levels of expression in the major burn and normal samples in the GSE37069 dataset (Fig. 5). Among them, EPAS1, PINK1, NME8, MMP9, IL10, and GCLM are highly expressed in the major burn samples, while the other genes are lowly expressed in the major burn samples. Then, ROC curve analysis was used to test the ability of each hub gene to differentiate major burn samples from control samples based on expression levels (Fig. 6A), and candidate genes with the best discriminatory performance (ROC value > 0.8) were selected. It was found that the AUC of ROC for all 10 hub genes exceeded 0.8. Among them, MMP9 had the highest AUC, which was 0.966, and GCLC had the lowest AUC, which was 0.844. Co-expression analysis revealed the most significant positive correlation between SIRT1 and GCLC, IL10 and GCLM(Fig. 6B-D).

Immunoinfiltration analysis of hub genes

This study analyzed the effects of hub genes on the immune system after severe burns and conducted an immunoinfiltration scoring. Among the 28 types of immune cells, MMP9 and BCL2 were significantly correlated with multiple immune cells(Fig. 7). MMP9 was positively correlated with plasmacytoid dendritic cell, macrophage, regulatory T cell, and activated dendritic cell, but negatively correlated with activated CD8 T cell, effector memory CD8 T cell, MDSC, CD56bright natural killer cell, natural killer T cell, natural killer cell, eosinophil, type 2 T helper cell, type 1 T helper cell, central memory CD4 T cell, T follicular helper cell, effector memory CD4 T cell, memory B cell, CD56dim natural killer cell, neutrophil, immature B cell, and activated CD4 T cell. BCL2 was significantly positively correlated with natural killer T cell, activated B cell, activated CD4 T cell, MDSC, T follicular helper cell, central memory CD4 T cell, CD56bright natural killer cell, CD56dim natural killer cell, natural killer cell, type 1 T helper cell, effector memory CD8 T cell, and activated CD8 T cell, but significantly negatively correlated with activated dendritic cell, macrophage, regulatory T cell, mast cell, and neutrophil.

Gene set enrichment analysis

According to the GSEA results, the MMP9 high-expression group was enriched in amylopectin and sucrose metabolism, lysosome and regulation of cytoskeleton by actin, while the BCL2 high-expression group was highly enriched in lysosome, graft versus host disease, and primary immune deficiency. The GCLM high-expression group was enriched in amylopectin and sucrose metabolism, amino sugar and nucleotide sugar metabolism, and cell cycle. The enrichment status of other high-expression gene groups is shown in the figure (Fig. 8).

Prediction of drugs, miRNAs, and transcription factors based on hub genes

Based on the hub genes, we predicted 37 target drugs. For example, the target drug for GCLM is cisplatin, and the target drugs predicted by MMP9 include curcumin, marimastat, and bevacizumab. BCL2 predicted target drugs include paclitaxel, vinorelbine, cisplatin, and isosorbide dinitrate, with a total of 19 drugs. The target drugs predicted for IL-10 include retinoic acid, sirolimus, tacrolimus, and letrozole. SIRT1 predicted target drugs include sulforaphane, rapamycin, and resveratrol, and GCLC predicted target drugs include cisplatin and cisplatin (Fig. 9A).

Furthermore, utilizing the miRNet database, a hub gene-miRNA regulatory network and a hub gene-transcription factor (TF) network were generated, identifying 458 targeted miRNAs and 19 TFs (Fig. 9B,C).

Multi-gene diagnostic model and independent analysis validation

Based on the dataset GSE37069, we established a multi-gene diagnostic model using LASSO regression and ROC analysis, focusing on 10 hub genes (Fig. 10). The AUC of the ROC curve is 0.988, indicating excellent discriminatory performance. Furthermore, the confusion matrix shows an accuracy of 0.96, precision of 0.96, and recall of 0.99 for this model (Fig. 11A, B). In the validation dataset, the accuracy is 0.99, precision is 0.99, and recall is 0.99. Additionally, the AUC of the ROC curve is 0.988, indicating good practicality of the model (Fig. 11C, D).

Burn injury is an underestimated form of damage that is associated with high rates of morbidity and mortality. Burns, especially major burns, are accompanied by immune and inflammatory responses, metabolic changes, and distributive shock, which are difficult to control and can potentially result in multi-organ failure^[18]. Oxidative stress is an important factor in the pathophysiological changes that occur in burn injuries^[19]. Oxidative stress is an imbalance between free radicals and the antioxidant system, leading to overproduction of free radicals (including oxygen molecules) and ultimately resulting in oxidative damage in areas such as lipid peroxidation, protein denaturation, and DNA mutations^[20]. Due to the severe oxidative stress response and pathophysiological disorders that occur after major burn injury, identifying targets of oxidative stress is one of the prospects for burn treatment^[21-23]. In this study, a variety of oxidative stress biomarkers were identified by comparing the gene expression profiles of severely burned patients and normal controls. Transcriptional datasets were retrieved from the GEO database, and through the integration of machine learning, WGCNA (Weighted Gene Co-expression Network Analysis), and PPI (Protein-Protein Interaction) network analysis, a set of 10 central genes related to immunity and oxidative stress were determined. These genes are GCLM, MMP9, BCL2, IL10, SIRT1, NME8, PINK1, GCLC, EPAS1, and PARK7. Several hub genes, such as GCLM, PINK1, and IL10, are located on chromosome 1.The diagnostic value of hub genes was evaluated using ROC curve analysis. Furthermore, these hub genes were validated using another dataset, which showed that all 10 hub genes exhibited excellent diagnostic performance.

GCL is the rate-limiting enzyme in the intracellular biosynthesis of glutathione (GSH). It consists of two subunits encoded by different genes, namely glutamate-cysteine ligase catalytic (GCLC) and glutamate-cysteine ligase modifier subunit (GCLM)^[24-26]. GCLC plays a catalytic role and is inhibited by feedback from GSH concentration. GCLM itself does not have enzymatic activity, but it regulates the affinity of GCLC for glutamate by reducing the Km value of glutamate and increasing the Ki value of GSH, thereby regulating intracellular GSH concentration. The redox state of the cell can influence GCL activity and thus regulate GSH formation. Changes in GCL activity may result from multiple levels of regulation of GCLC or both GCLC and GCLM. In this study, an increase in GCLM expression and a decrease in GCLC expression levels may be a result of feedback regulation by GSH.

Matrix metalloproteinase-9 (MMP-9) is a member of the metzincin family and is primarily an extracellular protease^[27]. Multiple studies have shown that MMP-9 is associated with various diseases. The increase of MMP9 after myocardial infarction (MI) inhibits autophagy activity in CHF and exacerbates ischemia-induced chronic heart failure (CHF)^[28].

Members of the Bcl-2 protein family are key regulators of apoptosis and cell death. The family is divided into anti-apoptotic members (including Bcl-2 itself, Bcl-xL, Mcl-1, etc.) and pro-apoptotic members (Bax, Bak, Bad, etc.). Their typical role in the mitochondria in regulating mitochondrial outer membrane permeability and cell apoptosis is well known^{[29, 30]}. In this study, BCL2 expression was reduced and the anti-apoptotic level of the organism was decreased after major burn.

The cytokine IL-10 is a crucial anti-inflammatory mediator that plays a central role in infections, ensuring the host is protected from excessive reactions to pathogens and the microbiota. Additionally, it plays an important role in sterile wound healing, autoimmunity, cancer, and maintaining homeostasis in the body^{[31, 32]}. After major burns, the body’s inflammatory response is enhanced, resulting in an increase in IL-10 levels to regulate the immune system.

Sirtuins are a class III histone deacetylase, and the deacetylation of histones by sirtuins depends on nicotinamide adenine dinucleotide (NAD+). Among the seven sirtuins, SIRT1 plays a key role in regulating various physiological processes, including cell apoptosis, DNA repair, inflammation, metabolism, cancer, and stress. It is also considered as one of the therapeutic targets for age-related diseases^{[33, 34]}. In this study, the expression of SIRT1 was found to be reduced, and it is expected that intervention on this factor may regulate the body’s oxidative stress response.

The NME protein family consists of 10 subtypes, named NME1-10, each with different enzymatic activities and subcellular localization patterns. Each subtype contains a conserved domain associated with nucleoside diphosphate kinases (NDPK), although not all domains possess catalytic activity. Several NME subtypes (NME1, NME5, NME7, and NME8) also exhibit 3’-5’ exonuclease activity, indicating their involvement in DNA proofreading and repair^[35]. NME8 is also implicated in Alzheimer’s disease^[36]. In this study, the level of NME8 was significantly elevated, suggesting its association with protein repair following trauma.

Phosphatase and tensin homolog-induced kinase 1 (PINK1) plays many important roles in cells, participating in the regulation of various cellular physiological and pathological processes, with a prominent role in regulating mitochondrial function. PINK1 triggers mitochondrial autophagy, a process of selective autophagy of mitochondria, and is involved in mitochondrial regeneration. PINK1 has also received significant attention due to its role in early-onset Parkinson’s disease^{[37, 38]}. In this study, a significant decrease in the expression level of PINK1 suggests mitochondrial dysfunction following major burns.

EPAS1, also known as hypoxia-inducible factor-2α (HIF-2α), acts as a transcription factor involved in various cellular pathways. Its expression level is strongly correlated with the infiltration of several types of immune cells, including γδT cells, T follicular helper cells, CD8+ T cells, and CD4+ T cells. EPAS1 is closely associated with the occurrence and development of PH and other related cardiovascular diseases^{[39, 40]}. Due to hypoxia and inadequate tissue perfusion following major burns, the expression level of EPAS1 was significantly decreased.

PARK7 is a highly conserved multifunctional enzyme in humans, with complex enzymatic activities and non-enzymatic functions such as antioxidation, anti-glycation, protein quality control, and its role as a transcription co-activator. These properties allow PARK7 to play important regulatory roles in various cellular processes, including epigenetic regulation, making it a promising therapeutic target for various diseases, especially cancer and Parkinson’s disease. The downregulation of PARK7 expression in major burns also suggests it could be a potential target for treating oxidative stress following burn injuries.

As is well known, major burn injuries cause a severe oxidative stress response. In this study, we first identified 10 hub genes targeting oxidative stress (OS) processes in major burns through bioinformatics analysis. We then performed GO analysis and KEGG enrichment analysis, as well as immune cell infiltration analysis, to establish a diagnostic model and analyze its accuracy. Although further experimental validation is needed to confirm the functions related to OS and major burns, the high performance of the diagnostic model has been validated. Finally, we predicted downstream transcriptional regulatory targets and upstream regulatory factors (including many miRNAs) involved in the OS response in major burns. It is noteworthy that there are still several drugs targeting these central genes that have not been demonstrated to alleviate the oxidative stress response following burn injuries, indicating that the candidate hubs could provide new avenues for future research.

Data availability statement

The dataset supporting the conclusions of this study can be obtained from the GEO website (https://www.ncbi. nlm.nih.gov/geo/). The data accession numbers are GSE37069 and GSE19743.

Author contributions

WBH and XW designed the study, collected the original data, finished the analysis and drafted the initial manuscript; XYH, YFL, ZYD, ZFL and HW assisted in data analysis; ZXX and GNZ assist with data processing and writing, HPH and ZHF guided the data analysis and writing. The final manuscript was read and approved by all authors. All authors approved the submitted version.

Funding

This research was supported by National Natural Science Foundation of China (Grant No. 82160380). Jiangxi Provincial Natural Science Foundation of China (20232BAB206082)，The Top-level Clinical Discipline Project of Shanghai Pudong (PWYgf2018-05)and The National Key R&D Program of China (2019YFA0110601) .

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No competing interests reported.

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Identification of differentially expressed oxidative stress-related genes in major burned patients and potential mechanisms based on hub genes

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